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Li S, Xie BB, Yin BW, Liu L, Shen L, Fang WH. Construction of Highly Accurate Machine Learning Potential Energy Surfaces for Excited-State Dynamics Simulations Based on Low-Level Data Sets. J Phys Chem A 2024; 128:5516-5524. [PMID: 38954640 DOI: 10.1021/acs.jpca.4c02028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Machine learning is capable of effectively predicting the potential energies of molecules in the presence of high-quality data sets. Its application in the construction of ground- and excited-state potential energy surfaces is attractive to accelerate nonadiabatic molecular dynamics simulations of photochemical reactions. Because of the huge computational cost of excited-state electronic structure calculations, the construction of a high-quality data set becomes a bottleneck. In the present work, we first built two data sets. One was obtained from surface hopping dynamics simulations at the semiempirical OM2/MRCI level. Another was extracted from the dynamics trajectories at the CASSCF level, which was reported previously. The ground- and excited-state potential energy surfaces of ethylene-bridged azobenzene at the CASSCF computational level were constructed based on the former low-level data set. Although non-neural network machine learning methods can achieve good or modest performance during the training process, only neural network models provide reliable predictions on the latter external test data set. The BPNN and SchNet combined with the Δ-ML scheme and the force term in the loss functions are recommended for dynamics simulations. Then, we performed excited-state dynamics simulations of the photoisomerization of ethylene-bridged azobenzene on machine learning potential energy surfaces. Compared with the lifetimes of the first excited state (S1) estimated at different computational levels, our results on the E isomer are in good agreement with the high-level estimation. However, the overestimation of the Z isomer is unimproved. It suggests that smaller errors during the training process do not necessarily translate to more accurate predictions on high-level potential energies or better performance on nonadiabatic dynamics simulations, at least in the present case.
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Affiliation(s)
- Shuai Li
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China
| | - Bin-Bin Xie
- Hangzhou Institute of Advanced Studies, Zhejiang Normal University, Hangzhou 311231, Zhejiang, P. R. China
| | - Bo-Wen Yin
- Hangzhou Institute of Advanced Studies, Zhejiang Normal University, Hangzhou 311231, Zhejiang, P. R. China
| | - Lihong Liu
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China
| | - Lin Shen
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China
- Yantai-Jingshi Institute of Material Genome Engineering, Yantai 265505, Shandong, P. R. China
| | - Wei-Hai Fang
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, P. R. China
- Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing, Yantai 264006, Shandong, P. R. China
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2
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Aldossary A, Campos-Gonzalez-Angulo JA, Pablo-García S, Leong SX, Rajaonson EM, Thiede L, Tom G, Wang A, Avagliano D, Aspuru-Guzik A. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402369. [PMID: 38794859 DOI: 10.1002/adma.202402369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/28/2024] [Indexed: 05/26/2024]
Abstract
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
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Affiliation(s)
- Abdulrahman Aldossary
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | | | - Sergio Pablo-García
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
| | - Shi Xuan Leong
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Ella Miray Rajaonson
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Luca Thiede
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Gary Tom
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Andrew Wang
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Davide Avagliano
- Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences (iCLeHS UMR 8060), Paris, F-75005, France
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
- Department of Materials Science & Engineering, University of Toronto, 184 College St., Toronto, ON, M5S 3E4, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Toronto, ON, M5S 3E5, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 66118 University Ave., Toronto, M5G 1M1, Canada
- Acceleration Consortium, 80 St George St, Toronto, M5S 3H6, Canada
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3
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Zhang L, Pios SV, Martyka M, Ge F, Hou YF, Chen Y, Chen L, Jankowska J, Barbatti M, Dral PO. MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods. J Chem Theory Comput 2024; 20:5043-5057. [PMID: 38836623 DOI: 10.1021/acs.jctc.4c00468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out-of-the-box. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in dozens of lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.
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Affiliation(s)
- Lina Zhang
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Sebastian V Pios
- Zhejiang Laboratory, Hangzhou, Zhejiang 311100, People's Republic of China
| | - Mikołaj Martyka
- Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland
| | - Fuchun Ge
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Yi-Fan Hou
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Yuxinxin Chen
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
| | - Lipeng Chen
- Zhejiang Laboratory, Hangzhou, Zhejiang 311100, People's Republic of China
| | - Joanna Jankowska
- Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland
| | - Mario Barbatti
- Aix Marseille University, CNRS, ICR, Marseille 13397, France
- Institut Universitaire de France, Paris 75231, France
| | - Pavlo O Dral
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, Fujian 361005, China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
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4
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Bassani CL, van Anders G, Banin U, Baranov D, Chen Q, Dijkstra M, Dimitriyev MS, Efrati E, Faraudo J, Gang O, Gaston N, Golestanian R, Guerrero-Garcia GI, Gruenwald M, Haji-Akbari A, Ibáñez M, Karg M, Kraus T, Lee B, Van Lehn RC, Macfarlane RJ, Mognetti BM, Nikoubashman A, Osat S, Prezhdo OV, Rotskoff GM, Saiz L, Shi AC, Skrabalak S, Smalyukh II, Tagliazucchi M, Talapin DV, Tkachenko AV, Tretiak S, Vaknin D, Widmer-Cooper A, Wong GCL, Ye X, Zhou S, Rabani E, Engel M, Travesset A. Nanocrystal Assemblies: Current Advances and Open Problems. ACS NANO 2024; 18:14791-14840. [PMID: 38814908 DOI: 10.1021/acsnano.3c10201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.
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Affiliation(s)
- Carlos L Bassani
- Institute for Multiscale Simulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Greg van Anders
- Department of Physics, Engineering Physics, and Astronomy, Queen's University, Kingston, Ontario K7L 3N6, Canada
| | - Uri Banin
- Institute of Chemistry and the Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Dmitry Baranov
- Division of Chemical Physics, Department of Chemistry, Lund University, SE-221 00 Lund, Sweden
| | - Qian Chen
- University of Illinois, Urbana, Illinois 61801, USA
| | - Marjolein Dijkstra
- Soft Condensed Matter & Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CC Utrecht, The Netherlands
| | - Michael S Dimitriyev
- Department of Polymer Science and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, USA
- Department of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, USA
| | - Efi Efrati
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel
- James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA
| | - Jordi Faraudo
- Institut de Ciencia de Materials de Barcelona (ICMAB-CSIC), Campus de la UAB, E-08193 Bellaterra, Barcelona, Spain
| | - Oleg Gang
- Department of Chemical Engineering, Columbia University, New York, New York 10027, USA
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Nicola Gaston
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Department of Physics, The University of Auckland, Auckland 1142, New Zealand
| | - Ramin Golestanian
- Max Planck Institute for Dynamics and Self-Organization (MPI-DS), 37077 Göttingen, Germany
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, UK
| | - G Ivan Guerrero-Garcia
- Facultad de Ciencias de la Universidad Autónoma de San Luis Potosí, 78295 San Luis Potosí, México
| | - Michael Gruenwald
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, USA
| | - Amir Haji-Akbari
- Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut 06511, USA
| | - Maria Ibáñez
- Institute of Science and Technology Austria (ISTA), 3400 Klosterneuburg, Austria
| | - Matthias Karg
- Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany
| | - Tobias Kraus
- INM - Leibniz-Institute for New Materials, 66123 Saarbrücken, Germany
- Saarland University, Colloid and Interface Chemistry, 66123 Saarbrücken, Germany
| | - Byeongdu Lee
- X-ray Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Reid C Van Lehn
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53717, USA
| | - Robert J Macfarlane
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, USA
| | - Bortolo M Mognetti
- Center for Nonlinear Phenomena and Complex Systems, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Arash Nikoubashman
- Leibniz-Institut für Polymerforschung Dresden e.V., 01069 Dresden, Germany
- Institut für Theoretische Physik, Technische Universität Dresden, 01069 Dresden, Germany
| | - Saeed Osat
- Max Planck Institute for Dynamics and Self-Organization (MPI-DS), 37077 Göttingen, Germany
| | - Oleg V Prezhdo
- Department of Chemistry, University of Southern California, Los Angeles, CA 90089, USA
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA
| | - Grant M Rotskoff
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Leonor Saiz
- Department of Biomedical Engineering, University of California, Davis, California 95616, USA
| | - An-Chang Shi
- Department of Physics & Astronomy, McMaster University, Hamilton, Ontario L8S 4M1, Canada
| | - Sara Skrabalak
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
| | - Ivan I Smalyukh
- Department of Physics and Chemical Physics Program, University of Colorado, Boulder, Colorado 80309, USA
- International Institute for Sustainability with Knotted Chiral Meta Matter, Hiroshima University, Higashi-Hiroshima City 739-0046, Japan
| | - Mario Tagliazucchi
- Universidad de Buenos Aires, Ciudad Universitaria, C1428EHA Ciudad Autónoma de Buenos Aires, Buenos Aires 1428 Argentina
| | - Dmitri V Talapin
- Department of Chemistry, James Franck Institute and Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, USA
- Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Alexei V Tkachenko
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Sergei Tretiak
- Theoretical Division and Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - David Vaknin
- Iowa State University and Ames Lab, Ames, Iowa 50011, USA
| | - Asaph Widmer-Cooper
- ARC Centre of Excellence in Exciton Science, School of Chemistry, University of Sydney, Sydney, New South Wales 2006, Australia
- The University of Sydney Nano Institute, University of Sydney, Sydney, New South Wales 2006, Australia
| | - Gerard C L Wong
- Department of Bioengineering, University of California, Los Angeles, California 90095, USA
- Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095, USA
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
| | - Xingchen Ye
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA
| | - Shan Zhou
- Department of Nanoscience and Biomedical Engineering, South Dakota School of Mines and Technology, Rapid City, South Dakota 57701, USA
| | - Eran Rabani
- Department of Chemistry, University of California and Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- The Raymond and Beverly Sackler Center of Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Michael Engel
- Institute for Multiscale Simulation, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Alex Travesset
- Iowa State University and Ames Lab, Ames, Iowa 50011, USA
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5
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Montero-Cabrera LA, Montero-Alejo AL, Aspuru-Guzik A, García de la Vega JM, Piris M, Díaz-Fernández LA, Pérez-Badell Y, Guerra-Barroso A, Alfonso-Ramos JE, Rodríguez J, Fuentes ME, de Armas CM. Alternative CNDOL Fockians for fast and accurate description of molecular exciton properties. J Chem Phys 2024; 160:214108. [PMID: 38828812 DOI: 10.1063/5.0208809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
CNDOL is an a priori, approximate Fockian for molecular wave functions. In this study, we employ several modes of singly excited configuration interaction (CIS) to model molecular excitation properties by using four combinations of the one electron operator terms. Those options are compared to the experimental and theoretical data for a carefully selected set of molecules. The resulting excitons are represented by CIS wave functions that encompass all valence electrons in the system for each excited state energy. The Coulomb-exchange term associated to the calculated excitation energies is rationalized to evaluate theoretical exciton binding energies. This property is shown to be useful for discriminating the charge donation ability of molecular and supermolecular systems. Multielectronic 3D maps of exciton formal charges are showcased, demonstrating the applicability of these approximate wave functions for modeling properties of large molecules and clusters at nanoscales. This modeling proves useful in designing molecular photovoltaic devices. Our methodology holds potential applications in systematic evaluations of such systems and the development of fundamental artificial intelligence databases for predicting related properties.
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Affiliation(s)
- Luis A Montero-Cabrera
- Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana, 10400 Havana, Cuba
- Donostia International Physics Center (DIPC), 20018 Donostia - San Sebastián, Basque Country, Spain
| | - Ana L Montero-Alejo
- Departamento de Física, Facultad de Ciencias Naturales, Matemática y del Medio Ambiente (FCNMM), Universidad Tecnológica Metropolitana; Ñuñoa, Santiago 7800002, Chile
| | - Alan Aspuru-Guzik
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | | | - Mario Piris
- Donostia International Physics Center (DIPC), 20018 Donostia - San Sebastián, Basque Country, Spain
| | - Lourdes A Díaz-Fernández
- Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana, 10400 Havana, Cuba
| | - Yoana Pérez-Badell
- Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana, 10400 Havana, Cuba
| | - Alberto Guerra-Barroso
- Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana, 10400 Havana, Cuba
| | - Javier E Alfonso-Ramos
- Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana, 10400 Havana, Cuba
| | - Javier Rodríguez
- Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana, 10400 Havana, Cuba
| | - María E Fuentes
- Facultad de Ciencias Químicas, Universidad Autónoma de Chihuahua, Chihuahua, 31100 Chihuahua, Mexico
| | - Carlos M de Armas
- Laboratorio de Química Computacional y Teórica, Departamento de Química Física, Universidad de La Habana, 10400 Havana, Cuba
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6
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Lu XY, Wu HP, Ma H, Li H, Li J, Liu YT, Pan ZY, Xie Y, Wang L, Ren B, Liu GK. Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives. Anal Chem 2024; 96:7959-7975. [PMID: 38662943 DOI: 10.1021/acs.analchem.4c01639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.
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Affiliation(s)
- Xin-Yu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hao-Ping Wu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
| | - Hao Ma
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hui Li
- Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen 361005, P. R. China
| | - Jia Li
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, P. R. China
| | - Yan-Ti Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Zheng-Yan Pan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yi Xie
- School of Informatics, Xiamen University, Xiamen 361005, P. R. China
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, P. R. China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
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7
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Arcidiacono A, Cignoni E, Mazzeo P, Cupellini L, Mennucci B. Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning. J Phys Chem A 2024; 128:3646-3658. [PMID: 38683801 PMCID: PMC11089512 DOI: 10.1021/acs.jpca.4c00249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/03/2024] [Accepted: 04/01/2024] [Indexed: 05/02/2024]
Abstract
Solvatochromism occurs in both homogeneous solvents and more complex biological environments, such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes more complicated not only because of structural effects induced by the protein pocket but also because the protein environment is highly anisotropic. This is particularly evident for highly conjugated and flexible molecules such as carotenoids, whose excitation energy is strongly dependent on both the geometry and the electrostatics of the environment. Here, we introduce a machine learning (ML) strategy trained on quantum mechanics/molecular mechanics calculations of geometrical and electrochromic contributions to carotenoids' excitation energies. We employ this strategy to compare solvatochromism in protein and solvent environments. Despite the important specifities of the protein, ML models trained on solvents can faithfully predict excitation energies in the protein environment, demonstrating the robustness of the chosen descriptors.
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Affiliation(s)
- Amanda Arcidiacono
- Department of Chemistry and Industrial
Chemistry, University of Pisa, Via G. Moruzzi 13, 56124 Pisa, Italy
| | - Edoardo Cignoni
- Department of Chemistry and Industrial
Chemistry, University of Pisa, Via G. Moruzzi 13, 56124 Pisa, Italy
| | - Patrizia Mazzeo
- Department of Chemistry and Industrial
Chemistry, University of Pisa, Via G. Moruzzi 13, 56124 Pisa, Italy
| | - Lorenzo Cupellini
- Department of Chemistry and Industrial
Chemistry, University of Pisa, Via G. Moruzzi 13, 56124 Pisa, Italy
| | - Benedetta Mennucci
- Department of Chemistry and Industrial
Chemistry, University of Pisa, Via G. Moruzzi 13, 56124 Pisa, Italy
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8
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Xie J, Zhou Y, Faizan M, Li Z, Li T, Fu Y, Wang X, Zhang L. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies. NATURE COMPUTATIONAL SCIENCE 2024; 4:322-333. [PMID: 38783137 DOI: 10.1038/s43588-024-00632-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.
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Affiliation(s)
- Jiahao Xie
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yansong Zhou
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Muhammad Faizan
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Zewei Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Tianshu Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yuhao Fu
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Xinjiang Wang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
| | - Lijun Zhang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
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9
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Cignoni E, Suman D, Nigam J, Cupellini L, Mennucci B, Ceriotti M. Electronic Excited States from Physically Constrained Machine Learning. ACS CENTRAL SCIENCE 2024; 10:637-648. [PMID: 38559300 PMCID: PMC10979507 DOI: 10.1021/acscentsci.3c01480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/16/2024] [Accepted: 01/30/2024] [Indexed: 04/04/2024]
Abstract
Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly with physically grounded operations. We present an example of an integrated modeling approach in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those on which it is trained and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parametrization corresponding to a minimal atom-centered basis. These results emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency and providing a blueprint for developing ML-augmented electronic-structure methods.
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Affiliation(s)
- Edoardo Cignoni
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, 56126 Pisa, Italy
| | - Divya Suman
- Laboratory
of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
| | - Jigyasa Nigam
- Laboratory
of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
| | - Lorenzo Cupellini
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, 56126 Pisa, Italy
| | - Benedetta Mennucci
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, 56126 Pisa, Italy
| | - Michele Ceriotti
- Laboratory
of Computational Science and Modeling, Institut des Matériaux, École Polytechnique Fédérale
de Lausanne, 1015 Lausanne, Switzerland
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
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10
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Dral PO. AI in computational chemistry through the lens of a decade-long journey. Chem Commun (Camb) 2024; 60:3240-3258. [PMID: 38444290 DOI: 10.1039/d4cc00010b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.
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Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
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11
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Jung SG, Jung G, Cole JM. Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks. J Chem Inf Model 2024; 64:1486-1501. [PMID: 38422386 PMCID: PMC10934802 DOI: 10.1021/acs.jcim.3c01792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024]
Abstract
Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to data-driven techniques. Although theoretical methods, such as time-dependent density functional theory (TD-DFT) calculations, have well-established physical relevance and are among the most popular methods in computational physics and chemistry, they exhibit errors that are inherent in their approximate nature. These high-throughput electronic structure calculations also incur a substantial computational cost. With the emergence of big-data initiatives, cost-effective, data-driven methods have gained traction, although their usability is highly contingent on the degree of data quality and sparsity. In this study, we present a workflow that employs deep residual convolutional neural networks (DR-CNN) and gradient boosting feature selection to predict peak optical absorption wavelengths (λmax) exclusively from SMILES representations of dye molecules and solvents; one would normally measure λmax using UV-vis absorption spectroscopy. We use a multifidelity modeling approach, integrating 34,893 DFT calculations and 26,395 experimentally derived λmax data, to deliver more accurate predictions via a Bayesian-optimized gradient boosting machine. Our approach is benchmarked against the state of the art that is reported in the scientific literature; results demonstrate that learnt representations via a DR-CNN workflow that is integrated with other machine learning methods can accelerate the design of molecules for specific optical characteristics.
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Affiliation(s)
- Son Gyo Jung
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
- ISIS
Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
- Research
Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, U.K.
| | - Guwon Jung
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
- Research
Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, U.K.
- Scientific
Computing Department, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
| | - Jacqueline M. Cole
- Cavendish
Laboratory, Department of Physics, University
of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, U.K.
- ISIS
Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
- Research
Complex at Harwell, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA, U.K.
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12
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Pios SV, Gelin MF, Ullah A, Dral PO, Chen L. Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra. J Phys Chem Lett 2024; 15:2325-2331. [PMID: 38386692 DOI: 10.1021/acs.jpclett.4c00107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Time-resolved spectroscopy is an important tool for unraveling the minute details of structural changes in molecules of biological and technological significance. The nonlinear femtosecond signals detected for such systems must be interpreted, but it is a challenging task for which theoretical simulations are often indispensable. Accurate simulations of transient absorption or two-dimensional electronic spectra are, however, computationally very expensive, prohibiting the wider adoption of existing first-principles methods. Here, we report an artificial-intelligence-enhanced protocol to drastically reduce the computational cost of simulating nonlinear time-resolved electronic spectra, which makes such simulations affordable for polyatomic molecules of increasing size. The protocol is based on the doorway-window approach for the on-the-fly surface-hopping simulations. We show its applicability for the prototypical molecule of pyrazine for which it produces spectra with high precision with respect to ab initio reference while cutting the computational cost by at least 95% compared to pure first-principles simulations.
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Affiliation(s)
- Sebastian V Pios
- Zhejiang Laboratory, Hangzhou, Zhejiang 311100, People's Republic of China
| | - Maxim F Gelin
- School of Science, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, People's Republic of China
| | - Arif Ullah
- School of Physics and Optoelectronic Engineering, Anhui University, Hefei, Anhui 230601, People's Republic of China
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, People's Republic of China
| | - Lipeng Chen
- Zhejiang Laboratory, Hangzhou, Zhejiang 311100, People's Republic of China
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13
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Mahato KD, Kumar U. Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 308:123768. [PMID: 38134661 DOI: 10.1016/j.saa.2023.123768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/05/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Applications of organic dyes, ranging from basic research to industry, are functions of their photophysical properties. Two important aspects- (1) knowledge of the photophysical properties of existing dyes long before real applications and (2) discovery of new organic dyes with desired photophysical properties for either upgradation of existing or development of new applications-are needed to be addressed. These two cases are coupled together with the common goal of estimating photophysical properties with high accuracy at the minimum cost of time and money long before the hard-core laboratory experiment. For this purpose, machine learning-based techniques are the most suitable approach. In this study, we used optimized machine-learning techniques to assess a dataset of 3066 organic dyes, which were evaluated using three evaluation parameters: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The Quadratic Support Vector Machine (QSVM) was the best predictive model for RMSE-16.614, MAE-10.837, and R2-0.961 for absorption wavelengths and RMSE-23.636, MAE-16.278, and R2-0.929 for emission wavelengths. These R2 values are 0.7% and 0.4% greater than the Gradient Boost Regression Tree (GBRT) model's recently reported values of 0.954 and 0.925 for absorption and emission wavelengths, respectively. Furthermore, we estimated the quantum yield and found that the Coarse Gaussian Support Vector Machine (CGSVM) outperformed all examined models. For more validation of these models, we compared the predicted results with the experimental results of selective dyes. The proposed automated approach can be used for predicting photophysical properties without much computer programming knowledge.
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Affiliation(s)
- Kapil Dev Mahato
- Department of Physics, National Institute of Technology Jamshedpur, Jharkhand 831014, India.
| | - Uday Kumar
- Department of Physics, National Institute of Technology Jamshedpur, Jharkhand 831014, India
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14
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Vennelakanti V, Kilic IB, Terrones GG, Duan C, Kulik HJ. Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes. J Phys Chem A 2024; 128:204-216. [PMID: 38148525 DOI: 10.1021/acs.jpca.3c07104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T1/2) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T1/2 values. We then compare ML T1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T1/2 values with a Pearson's r of 0.82. In contrast, DFA-predicted T1/2 values have large errors and show no correlation to estimated T1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.
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Affiliation(s)
- Vyshnavi Vennelakanti
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Irem B Kilic
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Gianmarco G Terrones
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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15
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do Casal MT, Veys K, Bousquet MHE, Escudero D, Jacquemin D. First-Principles Calculations of Excited-State Decay Rate Constants in Organic Fluorophores. J Phys Chem A 2023; 127:10033-10053. [PMID: 37988002 DOI: 10.1021/acs.jpca.3c06191] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
In this Perspective, we discuss recent advances made to evaluate from first-principles the excited-state decay rate constants of organic fluorophores, focusing on the so-called static strategy. In this strategy, one essentially takes advantage of Fermi's golden rule (FGR) to evaluate rate constants at key points of the potential energy surfaces, a procedure that can be refined in a variety of ways. In this way, the radiative rate constant can be straightforwardly obtained by integrating the fluorescence line shape, itself determined from vibronic calculations. Likewise, FGR allows for a consistent calculation of the internal conversion (related to the non-adiabatic couplings) in the weak-coupling regime and intersystem crossing rates, therefore giving access to estimates of the emission yields when no complex photophysical phenomenon is at play. Beyond outlining the underlying theories, we summarize here the results of benchmarks performed for various types of rates, highlighting that both the quality of the vibronic calculations and the accuracy of the relative energies are crucial to reaching semiquantitative estimates. Finally, we illustrate the successes and challenges in determining the fluorescence quantum yields using a series of organic fluorophores.
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Affiliation(s)
- Mariana T do Casal
- Department of Chemistry, Physical Chemistry and Quantum Chemistry Division, KU Leuven, 3001 Leuven, Belgium
| | - Koen Veys
- Department of Chemistry, Physical Chemistry and Quantum Chemistry Division, KU Leuven, 3001 Leuven, Belgium
| | | | - Daniel Escudero
- Department of Chemistry, Physical Chemistry and Quantum Chemistry Division, KU Leuven, 3001 Leuven, Belgium
| | - Denis Jacquemin
- Nantes Université, CNRS, CEISAM UMR 6230, F-44000 Nantes, France
- Institut Universitaire de France (IUF), FR-75005 Paris, France
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16
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Janoš J, Slavíček P. What Controls the Quality of Photodynamical Simulations? Electronic Structure Versus Nonadiabatic Algorithm. J Chem Theory Comput 2023; 19:8273-8284. [PMID: 37939301 PMCID: PMC10688183 DOI: 10.1021/acs.jctc.3c00908] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 11/10/2023]
Abstract
The field of nonadiabatic dynamics has matured over the last decade with a range of algorithms and electronic structure methods available at the moment. While the community currently focuses more on developing and benchmarking new nonadiabatic dynamics algorithms, the underlying electronic structure controls the outcome of nonadiabatic simulations. Yet, the electronic-structure sensitivity analysis is typically neglected. In this work, we present a sensitivity analysis of the nonadiabatic dynamics of cyclopropanone to electronic structure methods and nonadiabatic dynamics algorithms. In particular, we compare wave function-based CASSCF, FOMO-CASCI, MS- and XMS-CASPT2, density-functional REKS, and semiempirical MRCI-OM3 electronic structure methods with the Landau-Zener surface hopping, fewest switches surface hopping, and ab initio multiple spawning with informed stochastic selection algorithms. The results clearly demonstrate that the electronic structure choice significantly influences the accuracy of nonadiabatic dynamics for cyclopropanone even when the potential energy surfaces exhibit qualitative and quantitative similarities. Thus, selecting the electronic structure solely on the basis of the mapping of potential energy surfaces can be misleading. Conversely, we observe no discernible differences in the performance of the nonadiabatic dynamics algorithms across the various methods. Based on the above results, we discuss the present-day practice in computational photodynamics.
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Affiliation(s)
- Jiří Janoš
- Department of Physical Chemistry, University of Chemistry and Technology, Technická 5, 16628 Prague 6, Czech Republic
| | - Petr Slavíček
- Department of Physical Chemistry, University of Chemistry and Technology, Technická 5, 16628 Prague 6, Czech Republic
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17
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King DS, Truhlar DG, Gagliardi L. Variational Active Space Selection with Multiconfiguration Pair-Density Functional Theory. J Chem Theory Comput 2023; 19:8118-8128. [PMID: 37905518 DOI: 10.1021/acs.jctc.3c00792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
The selection of an adequate set of active orbitals for modeling strongly correlated electronic states is difficult to automate because it is highly dependent on the states and molecule of interest. Although many approaches have shown some success, no single approach has worked well in all cases. In light of this, we present the "discrete variational selection" (DVS) approach to active space selection, in which one generates multiple trial wave functions from a diverse set of systematically constructed active spaces and then selects between these wave functions variationally. We apply this DVS approach to 207 vertical excitations of small-to-medium-sized organic and inorganic molecules (with 3 to 18 atoms) in the QUESTDB database by (i) constructing various sets of active space orbitals through diagonalization of parametrized operators and (ii) choosing the result with the lowest average energy among the states of interest. This approach proves ineffective when variationally selecting between wave functions using the density matrix renormalization group (DMRG) or complete active space self-consistent field (CASSCF) energy but is able to provide good results when variationally selecting between wave functions using the energy of the translated PBE (tPBE) functional from multiconfiguration pair-density functional theory (MC-PDFT). Applying this DVS-tPBE approach to selection among state-averaged DMRG wave functions, we obtain a mean unsigned error of only 0.17 eV using hybrid MC-PDFT. This result matches that of our previous benchmark without the need to filter out poor active spaces and with no further orbital optimization following active space selection of the SA-DMRG wave functions. Furthermore, we find that DVS-tPBE is able to robustly and effectively select between the new SA-DMRG wave functions and our previous SA-CASSCF results.
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Affiliation(s)
- Daniel S King
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Group, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Laura Gagliardi
- Department of Chemistry, Pritzker School of Molecular Engineering, James Franck Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
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18
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Hennefarth MR, King DS, Gagliardi L. Linearized Pair-Density Functional Theory for Vertical Excitation Energies. J Chem Theory Comput 2023; 19:7983-7988. [PMID: 37877741 DOI: 10.1021/acs.jctc.3c00863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Multiconfiguration pair-density functional theory (MC-PDFT) is a computationally efficient method that computes the energies of electronic states in a state specific or state average framework via an on-top functional. However, MC-PDFT does not include state interaction among these states since the final energies do not come from the diagonalization of an effective model-space Hamiltonian. Recently, multistate extensions such as linearized PDFT (L-PDFT) have been developed to accurately model the potentials near conical intersections and avoided crossings. However, there has not been any systematic study evaluating their performance for predicting vertical excitations at the equilibrium geometry of a molecule, when the excited states are generally well separated. In this paper, we report the performance of L-PDFT on the extensive QUESTDB data set of vertical excitations using a database of automatically selected active spaces. We show that L-PDFT performs well on all these excitations and successfully reproduces the performance of MC-PDFT. These results further demonstrate the potential of L-PDFT, as its scaling is constant with the number of states included in the state-average manifold, whereas MC-PDFT scales linearly in this regard.
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Affiliation(s)
| | | | - Laura Gagliardi
- Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, United States
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19
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Yoo P, Bhowmik D, Mehta K, Zhang P, Liu F, Lupo Pasini M, Irle S. Deep learning workflow for the inverse design of molecules with specific optoelectronic properties. Sci Rep 2023; 13:20031. [PMID: 37973879 PMCID: PMC10654498 DOI: 10.1038/s41598-023-45385-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023] Open
Abstract
The inverse design of novel molecules with a desirable optoelectronic property requires consideration of the vast chemical spaces associated with varying chemical composition and molecular size. First principles-based property predictions have become increasingly helpful for assisting the selection of promising candidate chemical species for subsequent experimental validation. However, a brute-force computational screening of the entire chemical space is decidedly impossible. To alleviate the computational burden and accelerate rational molecular design, we here present an iterative deep learning workflow that combines (i) the density-functional tight-binding method for dynamic generation of property training data, (ii) a graph convolutional neural network surrogate model for rapid and reliable predictions of chemical and physical properties, and (iii) a masked language model. As proof of principle, we employ our workflow in the iterative generation of novel molecules with a target energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO).
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Affiliation(s)
- Pilsun Yoo
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
| | - Debsindhu Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Kshitij Mehta
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Pei Zhang
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Frank Liu
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Massimiliano Lupo Pasini
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Stephan Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
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20
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Vinod V, Maity S, Zaspel P, Kleinekathöfer U. Multifidelity Machine Learning for Molecular Excitation Energies. J Chem Theory Comput 2023; 19:7658-7670. [PMID: 37862054 DOI: 10.1021/acs.jctc.3c00882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
The accurate but fast calculation of molecular excited states is still a very challenging topic. For many applications, detailed knowledge of the energy funnel in larger molecular aggregates is of key importance, requiring highly accurate excitation energies. To this end, machine learning techniques can be a very useful tool, though the cost of generating highly accurate training data sets still remains a severe challenge. To overcome this hurdle, this work proposes the use of multifidelity machine learning where very little training data from high accuracies is combined with cheaper and less accurate data to achieve the accuracy of the costlier level. In the present study, the approach is employed to predict vertical excitation energies to the first excited state for three molecules of increasing size, namely, benzene, naphthalene, and anthracene. The energies are trained and tested for conformations stemming from classical molecular dynamics and density functional based tight-binding simulations. It can be shown that the multifidelity machine learning model can achieve the same accuracy as a machine learning model built only on high-cost training data while expending a much lower computational effort to generate the data. The numerical gain observed in these benchmark test calculations was over a factor of 30 but certainly can be much higher for high-accuracy data.
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Affiliation(s)
- Vivin Vinod
- School of Mathematics and Natural Science, University of Wuppertal, Wuppertal 42119, Germany
- School of Computer Science and Engineering, Constructor University, Campus Ring 1, Bremen 28759, Germany
| | - Sayan Maity
- School of Science, Constructor University, Campus Ring 1, Bremen 28759, Germany
| | - Peter Zaspel
- School of Mathematics and Natural Science, University of Wuppertal, Wuppertal 42119, Germany
- School of Computer Science and Engineering, Constructor University, Campus Ring 1, Bremen 28759, Germany
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21
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Ju CW, Wang XC, Li B, Ma Q, Shi Y, Zhang J, Xu Y, Peng Q, Zhao D. Evolution of organic phosphor through precision regulation of nonradiative decay. Proc Natl Acad Sci U S A 2023; 120:e2310883120. [PMID: 37934818 PMCID: PMC10655561 DOI: 10.1073/pnas.2310883120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/28/2023] [Indexed: 11/09/2023] Open
Abstract
Development of single-component organic phosphor attracts increasing interest due to its wide applications in optoelectronic technologies. Theoretically, activating efficient intersystem crossing (ISC) via 1(π, π*) to 3(π, π*) transitions, rather than 1(n, π*) → 3(π, π*) transitions, is an alternative access to purely organic phosphors but remains challenging. Herein, we designed and successfully synthesized the sila-8-membered ring fused biaryl benzoskeleton by transition metal catalysis, which served as a new organic phosphor with efficient 1(π, π*) to 3(π, π*) ISC. We first found that such a compound exhibits a record-long phosphorescence lifetime of 6.5 s at low temperature for single-component organic systems. Then, we developed two strategies to tune their decay channels to evolve such nonemissive molecules into bright phosphors with elongated lifetimes at room temperature: 1) Physic-based design, where quantitative analyses of electron-phonon coupling led us to reveal and hinder the major nonradiative channels, thus lighted up room temperature phosphorescence (RTP) with a lifetime of 480 ms at 298 K; 2) chemical geometry-driven molecular engineering, where a geometry-based descriptor ΔΘT1-S0/ΘS0 was developed for rational screening RTP candidates and further improved the RTP lifetime to 794 ms. This study clearly shows the power of interdiscipline among synthetic methodology, physics-based rational design, and computational modeling, which represents a paradigm for the development of an organic emitter.
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Affiliation(s)
- Cheng-Wei Ju
- State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin300071, People’s Republic of China
| | - Xi-Chao Wang
- State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin300071, People’s Republic of China
| | - Bo Li
- State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin300071, People’s Republic of China
| | - Qiushi Ma
- Department of Chemistry, Marquette University, Milwaukee, WI53233
| | - Yuhao Shi
- School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing100049, People’s Republic of China
| | - Jinyu Zhang
- State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin300071, People’s Republic of China
| | - Yuzhi Xu
- Department of Chemistry, New York University, New York, NY10003
| | - Qian Peng
- School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing100049, People’s Republic of China
| | - Dongbing Zhao
- State Key Laboratory and Institute of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin300071, People’s Republic of China
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22
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Chen Z, Wing-Wah Yam V. Encoding Hole-Particle Information in the Multi-Channel MolOrbImage for Machine-Learned Excited-State Energies of Large Photofunctional Materials. J Am Chem Soc 2023; 145:24098-24107. [PMID: 37874942 DOI: 10.1021/jacs.3c07766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
We present a novel class of one-electron multi-channel molecular orbital images (MolOrbImages) designed for the prediction of excited-state energetics in conjunction with the state-of-the-art VGG-type machine-learning architecture. By representing hole and particle states in the excitation process as channels of MolOrbImages, the revised VGG model achieves excellent prediction accuracy for both low-lying singlet and triplet states, with mean absolute errors (MAEs) of <0.08 and <0.1 eV for QM9 molecules and large photofunctional materials with up to 560 atoms, respectively. Remarkably, the model demonstrates exceptional performance (MAE < 1 kcal/mol) for the T1 state of QM9 molecules, making it a non-system-specific model that approaches chemical accuracy. The general rules attained, for instance, the improved performance with well-defined MO energies and the reduced overfitting concern via the inclusion of physically insightful hole-particle information, provide invaluable guidelines for the further design of orbital-based descriptors targeting molecular excited states.
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Affiliation(s)
- Ziyong Chen
- Institute of Molecular Functional Materials and Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Vivian Wing-Wah Yam
- Institute of Molecular Functional Materials and Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, China
- Hong Kong Quantum AI Lab Ltd., Hong Kong Science Park, Hong Kong, China
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23
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Mörsdorf JM, Ballmann J. Coordination-Induced Radical Generation: Selective Hydrogen Atom Abstraction via Controlled Ti-C σ-Bond Homolysis. J Am Chem Soc 2023; 145:23452-23460. [PMID: 37861658 DOI: 10.1021/jacs.3c05748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
A method for the generation of transient alkyl radicals via homolytic Ti-C bond cleavage was developed by employing a tailor-made organotitanium half-cage complex. In contrast to established metal-mediated radical initiation protocols via thermal or photochemical M-C σ-bond homolysis, radical formation is triggered solely by coordination of a solvent molecule (thf) to a titanium(IV) center. During the reaction, the nonstabilized alkyl radical is formed along with a persistent titanium(III) metalloradical, thus taming the former transient radical (persistent radical effect). Radical coupling and hydrogen atom abstraction (HAT) reactions have been explored not only experimentally but also computationally and by means of kinetic analysis. Exploiting these findings led to the development of selective HAT transformations, for example, with 9,10-dihydroanthracene. Deuterium labeling studies using selectively deuterated alkyls and 9,10-dihydroanthracene-d4 confirmed a radical pathway, which was underpinned by developing a radical-radical cross-coupling reaction for transferring the alkyl radical to a stable Sn-centered radical. To set the stage for an application in organic synthesis, a 5-endo-trig radical cyclization based on our methodology was established, and a dihydroxylated sesquiterpene was thus prepared in high diastereomeric excess.
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Affiliation(s)
- Jean-Marc Mörsdorf
- Anorganisch-Chemisches Institut, Universität Heidelberg, Im Neuenheimer Feld 276, D-69120 Heidelberg, Germany
| | - Joachim Ballmann
- Anorganisch-Chemisches Institut, Universität Heidelberg, Im Neuenheimer Feld 276, D-69120 Heidelberg, Germany
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24
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Polonius S, Zhuravel O, Bachmair B, Mai S. LVC/MM: A Hybrid Linear Vibronic Coupling/Molecular Mechanics Model with Distributed Multipole-Based Electrostatic Embedding for Highly Efficient Surface Hopping Dynamics in Solution. J Chem Theory Comput 2023; 19:7171-7186. [PMID: 37788824 PMCID: PMC10601485 DOI: 10.1021/acs.jctc.3c00805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Indexed: 10/05/2023]
Abstract
We present a theoretical framework for a hybrid linear vibronic coupling model electrostatically embedded into a molecular mechanics environment, termed the linear vibronic coupling/molecular mechanics (LVC/MM) method, for the surface hopping including arbitrary coupling (SHARC) molecular dynamics package. Electrostatic embedding is realized through the computation of interactions between environment point charges and distributed multipole expansions (DMEs, up to quadrupoles) that represent each electronic state and transition densities in the diabatic basis. The DME parameters are obtained through a restrained electrostatic potential (RESP) fit, which we extended to yield higher-order multipoles. We also implemented in SHARC a scheme for achieving roto-translational invariance of LVC models as well as a general quantum mechanics/molecular mechanics (QM/MM) interface, an OpenMM interface, and restraining potentials for simulating liquid droplets. Using thioformaldehyde in water as a test case, we demonstrate that LVC/MM can accurately reproduce the solvation structure and energetics of rigid solutes, with errors on the order of 1-2 kcal/mol compared to a BP86/MM reference. The implementation in SHARC is shown to be very efficient, enabling the simulation of trajectories on the nanosecond time scale in a matter of days.
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Affiliation(s)
- Severin Polonius
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Vienna
Doctoral School in Chemistry (DoSChem), University of Vienna, Währinger Str. 42, 1090 Vienna, Austria
| | - Oleksandra Zhuravel
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - Brigitta Bachmair
- Vienna
Doctoral School in Chemistry (DoSChem), University of Vienna, Währinger Str. 42, 1090 Vienna, Austria
- Research
Platform on Accelerating Photoreaction Discovery (ViRAPID), University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - Sebastian Mai
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
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25
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Li J, Wu N, Zhang J, Wu HH, Pan K, Wang Y, Liu G, Liu X, Yao Z, Zhang Q. Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction. NANO-MICRO LETTERS 2023; 15:227. [PMID: 37831203 PMCID: PMC10575847 DOI: 10.1007/s40820-023-01192-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/10/2023] [Indexed: 10/14/2023]
Abstract
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.
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Affiliation(s)
- Jin Li
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China
| | - Naiteng Wu
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China
| | - Jian Zhang
- New Energy Technology Engineering Lab of Jiangsu Province, College of Science, Nanjing University of Posts and Telecommunications (NUPT), Nanjing, 210023, People's Republic of China
| | - Hong-Hui Wu
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, People's Republic of China.
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, 8588, USA.
| | - Kunming Pan
- Henan Key Laboratory of High-Temperature Structural and Functional Materials, National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials, Henan University of Science and Technology, Luoyang, 471003, People's Republic of China
| | - Yingxue Wang
- National Engineering Laboratory for Risk Perception and Prevention, Beijing, 100041, People's Republic of China.
| | - Guilong Liu
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China
| | - Xianming Liu
- College of Chemistry and Chemical Engineering, and Henan Key Laboratory of Function-Oriented Porous Materials, Luoyang Normal University, Luoyang, 471934, People's Republic of China.
| | - Zhenpeng Yao
- Center of Hydrogen Science, Shanghai Jiao Tong University, Shanghai, 200000, People's Republic of China
- State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200000, People's Republic of China
| | - Qiaobao Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Materials, Xiamen University, Xiamen, 361005, People's Republic of China.
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26
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Lupo Pasini M, Mehta K, Yoo P, Irle S. Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules. Sci Data 2023; 10:546. [PMID: 37604820 PMCID: PMC10442335 DOI: 10.1038/s41597-023-02408-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/24/2023] [Indexed: 08/23/2023] Open
Abstract
We present two open-source datasets that provide time-dependent density-functional tight-binding (TD-DFTB) electronic excitation spectra of organic molecules. These datasets represent predictions of UV-vis absorption spectra performed on optimized geometries of the molecules in their electronic ground state. The GDB-9-Ex dataset contains a subset of 96,766 organic molecules from the original open-source GDB-9 dataset. The ORNL_AISD-Ex dataset consists of 10,502,904 organic molecules that contain between 5 and 71 non-hydrogen atoms. The data reveals the close correlation between the magnitude of the gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and the excitation energy of the lowest singlet excited state energies quantitatively. The chemical variability of the large number of molecules was examined with a topological fingerprint estimation based on extended-connectivity fingerprints (ECFPs) followed by uniform manifold approximation and projection (UMAP) for dimension reduction. Both datasets were generated using the DFTB+ software on the "Andes" cluster of the Oak Ridge Leadership Computing Facility (OLCF).
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Affiliation(s)
- Massimiliano Lupo Pasini
- Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, 37831, USA.
| | - Kshitij Mehta
- Oak Ridge National Laboratory, Computer Science and Mathematics Division, Oak Ridge, 37831, USA
| | - Pilsun Yoo
- Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, 37831, USA
| | - Stephan Irle
- Oak Ridge National Laboratory, Computational Sciences and Engineering Division, Oak Ridge, 37831, USA.
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27
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Chen MS, Mao Y, Snider A, Gupta P, Montoya-Castillo A, Zuehlsdorff TJ, Isborn CM, Markland TE. Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure. J Phys Chem Lett 2023; 14:6610-6619. [PMID: 37459252 DOI: 10.1021/acs.jpclett.3c01444] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. For chromophores in the condensed phase, the large number of atoms needed to simulate the environment has traditionally prohibited the use of high-level excited-state electronic structure methods. By leveraging transfer learning, we show how to construct machine-learned models to accurately predict the high-level excitation energies of a chromophore in solution from only 400 high-level calculations. We show that when the electronic excitations of the green fluorescent protein chromophore in water are treated using EOM-CCSD embedded in a DFT description of the solvent the optical spectrum is correctly captured and that this improvement arises from correctly treating the coupling of the electronic transition to electric fields, which leads to a larger response upon hydrogen bonding between the chromophore and water.
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Affiliation(s)
- Michael S Chen
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Yuezhi Mao
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Andrew Snider
- Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Prachi Gupta
- Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Andrés Montoya-Castillo
- Department of Chemistry, University of Colorado, Boulder, Boulder, Colorado 80309, United States
| | - Tim J Zuehlsdorff
- Department of Chemistry, Oregon State University, Corvallis, Oregon 97331, United States
| | - Christine M Isborn
- Chemistry and Biochemistry, University of California Merced, Merced, California 95343, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
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28
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Zhang J, Du W, Yang X, Wu D, Li J, Wang K, Wang Y. SMG-BERT: integrating stereoscopic information and chemical representation for molecular property prediction. Front Mol Biosci 2023; 10:1216765. [PMID: 37457837 PMCID: PMC10348360 DOI: 10.3389/fmolb.2023.1216765] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/15/2023] [Indexed: 07/18/2023] Open
Abstract
Molecular property prediction is a crucial task in various fields and has recently garnered significant attention. To achieve accurate and fast prediction of molecular properties, machine learning (ML) models have been widely employed due to their superior performance compared to traditional methods by trial and error. However, most of the existing ML models that do not incorporate 3D molecular information are still in need of improvement, as they are mostly poor at differentiating stereoisomers of certain types, particularly chiral ones. Also,routine featurization methods using only incomplete features are hard to obtain explicable molecular representations. In this paper, we propose the Stereo Molecular Graph BERT (SMG-BERT) by integrating the 3D space geometric parameters, 2D topological information, and 1D SMILES string into the self-attention-based BERT model. In addition, nuclear magnetic resonance (NMR) spectroscopy results and bond dissociation energy (BDE) are integrated as extra atomic and bond features to improve the model's performance and interpretability analysis. The comprehensive integration of 1D, 2D, and 3D information could establish a unified and unambiguous molecular characterization system to distinguish conformations, such as chiral molecules. Intuitively integrated chemical information enables the model to possess interpretability that is consistent with chemical logic. Experimental results on 12 benchmark molecular datasets show that SMG-BERT consistently outperforms existing methods. At the same time, the experimental results demonstrate that SMG-BERT is generalizable and reliable.
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Affiliation(s)
- Jiahui Zhang
- School of Software Engineering, University of Science and Technology of China, Hefei, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Wenjie Du
- School of Software Engineering, University of Science and Technology of China, Hefei, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Xiaoting Yang
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Di Wu
- School of Software Engineering, University of Science and Technology of China, Hefei, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Jiahe Li
- School of Software Engineering, University of Science and Technology of China, Hefei, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Kun Wang
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
| | - Yang Wang
- School of Software Engineering, University of Science and Technology of China, Hefei, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
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29
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Kabylda A, Vassilev-Galindo V, Chmiela S, Poltavsky I, Tkatchenko A. Efficient interatomic descriptors for accurate machine learning force fields of extended molecules. Nat Commun 2023; 14:3562. [PMID: 37322039 DOI: 10.1038/s41467-023-39214-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/17/2023] [Indexed: 06/17/2023] Open
Abstract
Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.
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Affiliation(s)
- Adil Kabylda
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Valentin Vassilev-Galindo
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Stefan Chmiela
- Machine Learning Group, Technische Universität Berlin, 10587, Berlin, Germany
- BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587, Berlin, Germany
| | - Igor Poltavsky
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.
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30
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Chen WK, Wang SR, Liu XY, Fang WH, Cui G. Nonadiabatic Derivative Couplings Calculated Using Information of Potential Energy Surfaces without Wavefunctions: Ab Initio and Machine Learning Implementations. Molecules 2023; 28:molecules28104222. [PMID: 37241962 DOI: 10.3390/molecules28104222] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
In this work, we implemented an approximate algorithm for calculating nonadiabatic coupling matrix elements (NACMEs) of a polyatomic system with ab initio methods and machine learning (ML) models. Utilizing this algorithm, one can calculate NACMEs using only the information of potential energy surfaces (PESs), i.e., energies, and gradients as well as Hessian matrix elements. We used a realistic system, namely CH2NH, to compare NACMEs calculated by this approximate PES-based algorithm and the accurate wavefunction-based algorithm. Our results show that this approximate PES-based algorithm can give very accurate results comparable to the wavefunction-based algorithm except at energetically degenerate points, i.e., conical intersections. We also tested a machine learning (ML)-trained model with this approximate PES-based algorithm, which also supplied similarly accurate NACMEs but more efficiently. The advantage of this PES-based algorithm is its significant potential to combine with electronic structure methods that do not implement wavefunction-based algorithms, low-scaling energy-based fragment methods, etc., and in particular efficient ML models, to compute NACMEs. The present work could encourage further research on nonadiabatic processes of large systems simulated by ab initio nonadiabatic dynamics simulation methods in which NACMEs are always required.
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Affiliation(s)
- Wen-Kai Chen
- Hebei Key Laboratory of Inorganic Nano-Materials, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, China
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Sheng-Rui Wang
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Xiang-Yang Liu
- College of Chemistry and Material Science, Sichuan Normal University, Chengdu 610068, China
| | - Wei-Hai Fang
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
- Hefei National Laboratory, Hefei 230088, China
| | - Ganglong Cui
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
- Hefei National Laboratory, Hefei 230088, China
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31
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Hung SH, Ye ZR, Cheng CF, Chen B, Tsai MK. Enhanced Predictions for the Experimental Photophysical Data Using the Featurized Schnet-Bondstep Approach. J Chem Theory Comput 2023. [PMID: 37126224 DOI: 10.1021/acs.jctc.3c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
An assessment of modifying the SchNET model for the predictions of experimental molecular photophysical properties, including absorption energy (ΔEabs), emission energy (ΔEemi), and photoluminescence quantum yield (PLQY), was reported. The solution environment was properly introduced outside the interaction layers of SchNET for not overly amplifying the solute-solvent interactions, particularly being supported by the changes of prediction errors between the presence and absence of the solvent effect. Two featurization schemes under the framework of the Schnet-bondstep approach, with featuring the concepts of reduced-atomic-number and reduced-atomic-neighbor, were demonstrated. These featurized models can consequently provide fine predictions for ΔEabs and ΔEemi with errors less than 0.1 eV. The corresponding predictions of PLQY were shown to be comparable to the previous graph convolution network model.
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Affiliation(s)
- Sheng-Hsuan Hung
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Zong-Rong Ye
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Chi-Feng Cheng
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Berlin Chen
- Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, Taiwan
| | - Ming-Kang Tsai
- Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan
- Department of Chemistry, Fu-Jen Catholic University, New Taipei City 24205, Taiwan
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32
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McNaughton AD, Joshi RP, Knutson CR, Fnu A, Luebke KJ, Malerich JP, Madrid PB, Kumar N. Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties. J Chem Inf Model 2023; 63:1462-1471. [PMID: 36847578 DOI: 10.1021/acs.jcim.2c01662] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods. In this work, we use both quantum mechanically (QM) predicted and experimentally measured UV-vis spectra as input to devise four different machine learning architectures, UVvis-SchNet, UVvis-DTNN, UVvis-Transformer, and UVvis-MPNN, and assess the performance of each method. We find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UV-vis spectra with a training RMSE of 0.06 and validation RMSE of 0.08. Most importantly, our model can be used for the challenging task of predicting differences in the UV-vis spectral signatures of regioisomers.
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Affiliation(s)
- Andrew D McNaughton
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Rajendra P Joshi
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Carter R Knutson
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Anubhav Fnu
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kevin J Luebke
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Jeremiah P Malerich
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Peter B Madrid
- SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States
| | - Neeraj Kumar
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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33
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Su Y, Dai Y, Zeng Y, Wei C, Chen Y, Ge F, Zheng P, Zhou D, Dral PO, Wang C. Interpretable Machine Learning of Two-Photon Absorption. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204902. [PMID: 36658720 PMCID: PMC10015897 DOI: 10.1002/advs.202204902] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/19/2022] [Indexed: 06/17/2023]
Abstract
Molecules with strong two-photon absorption (TPA) are important in many advanced applications such as upconverted laser and photodynamic therapy, but their design is hampered by the high cost of experimental screening and accurate quantum chemical (QC) calculations. Here a systematic study is performed by collecting an experimental TPA database with ≈900 molecules, analyzing with interpretable machine learning (ML) the key molecular features explaining TPA magnitudes, and building a fast ML model for predictions. The ML model has prediction errors of similar magnitude compared to experimental and affordable QC methods errors and has the potential for high-throughput screening as additionally validated with the new experimental measurements. ML feature analysis is generally consistent with common beliefs which is quantified and rectified. The most important feature is conjugation length followed by features reflecting the effects of donor and acceptor substitution and coplanarity.
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Affiliation(s)
- Yuming Su
- State Key Laboratory of Physical Chemistry of Solid SurfacesDepartment of ChemistryCollege of Chemistry and Chemical Engineering, iChemInnovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)Xiamen University361005XiamenP. R. China
| | - Yiheng Dai
- State Key Laboratory of Physical Chemistry of Solid SurfacesDepartment of ChemistryCollege of Chemistry and Chemical Engineering, iChemInnovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)Xiamen University361005XiamenP. R. China
| | - Yifan Zeng
- State Key Laboratory of Physical Chemistry of Solid SurfacesDepartment of ChemistryCollege of Chemistry and Chemical Engineering, iChemInnovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)Xiamen University361005XiamenP. R. China
| | - Caiyun Wei
- State Key Laboratory of Physical Chemistry of Solid SurfacesDepartment of ChemistryCollege of Chemistry and Chemical Engineering, iChemInnovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)Xiamen University361005XiamenP. R. China
| | - Yangtao Chen
- State Key Laboratory of Physical Chemistry of Solid SurfacesDepartment of ChemistryCollege of Chemistry and Chemical Engineering, iChemInnovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)Xiamen University361005XiamenP. R. China
| | - Fuchun Ge
- Department of ChemistryCollege of Chemistry and Chemical EngineeringiChemXiamen UniversityFujian Provincial Key Laboratory of Theoretical and Computational ChemistryXiamen University361005XiamenP. R. China
| | - Peikun Zheng
- Department of ChemistryCollege of Chemistry and Chemical EngineeringiChemXiamen UniversityFujian Provincial Key Laboratory of Theoretical and Computational ChemistryXiamen University361005XiamenP. R. China
| | - Da Zhou
- School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High‐Performance Scientific ComputationXiamen UniversityXiamen361005P. R. China
| | - Pavlo O. Dral
- Department of ChemistryCollege of Chemistry and Chemical EngineeringiChemXiamen UniversityFujian Provincial Key Laboratory of Theoretical and Computational ChemistryXiamen University361005XiamenP. R. China
| | - Cheng Wang
- State Key Laboratory of Physical Chemistry of Solid SurfacesDepartment of ChemistryCollege of Chemistry and Chemical Engineering, iChemInnovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)Xiamen University361005XiamenP. R. China
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34
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Chen Z, Yam VWW. Machine-Learned Electronically Excited States with the MolOrbImage Generated from the Molecular Ground State. J Phys Chem Lett 2023; 14:1955-1961. [PMID: 36787423 DOI: 10.1021/acs.jpclett.3c00014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
We present a general machine learning framework for probing the electronic state properties using the novel quantum descriptor MolOrbImage. Each pixel of the MolOrbImage records the quantum information generated by the integration of the physical operator with a pair of bra and ket molecular orbital (MO) states. Inspired by the success of deep convolutional neural networks (NNs) in computer vision, we have implemented the convolutional-layer-dominated MO-NN model. Using the orbital energy and electron repulsion integral MolOrbImages, the MO-NN model achieves promising prediction accuracies against the ADC(2)/cc-pVTZ reference for transition energies to both low-lying singlet [mean absolute error (MAE) < 0.16 eV] and triplet (MAE < 0.14 eV) states. An apparent improvement in the prediction of oscillator strength, which has been shown to be challenging previously, has been demonstrated in this study. Moreover, the transferability test indicates the remarkable extrapolation capacity of the MO-NN model to describe the out of data set systems.
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Affiliation(s)
- Ziyong Chen
- Institute of Molecular Functional Materials and Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong 999077, China
| | - Vivian Wing-Wah Yam
- Institute of Molecular Functional Materials and Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong 999077, China
- Hong Kong Quantum AI Lab Ltd., Hong Kong Science Park, Hong Kong 999077, China
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35
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Cignoni E, Cupellini L, Mennucci B. Machine Learning Exciton Hamiltonians in Light-Harvesting Complexes. J Chem Theory Comput 2023; 19:965-977. [PMID: 36701385 PMCID: PMC9933434 DOI: 10.1021/acs.jctc.2c01044] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Indexed: 01/27/2023]
Abstract
We propose a machine learning (ML)-based strategy for an inexpensive calculation of excitonic properties of light-harvesting complexes (LHCs). The strategy uses classical molecular dynamics simulations of LHCs in their natural environment in combination with ML prediction of the excitonic Hamiltonian of the embedded aggregate of pigments. The proposed ML model can reproduce the effects of geometrical fluctuations together with those due to electrostatic and polarization interactions between the pigments and the protein. The training is performed on the chlorophylls of the major LHC of plants, but we demonstrate that the model is able to extrapolate well beyond the initial training set. Moreover, the accuracy in predicting the effects of the environment is tested on the simulation of the small changes observed in the absorption spectra of the wild-type and a mutant of a minor LHC.
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Affiliation(s)
- Edoardo Cignoni
- Dipartimento di Chimica e
Chimica Industriale, University of Pisa, via G. Moruzzi 13, 56124Pisa, Italy
| | - Lorenzo Cupellini
- Dipartimento di Chimica e
Chimica Industriale, University of Pisa, via G. Moruzzi 13, 56124Pisa, Italy
| | - Benedetta Mennucci
- Dipartimento di Chimica e
Chimica Industriale, University of Pisa, via G. Moruzzi 13, 56124Pisa, Italy
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36
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Du W, Yang X, Wu D, Ma F, Zhang B, Bao C, Huo Y, Jiang J, Chen X, Wang Y. Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers. Brief Bioinform 2023; 24:bbac560. [PMID: 36528804 PMCID: PMC9851338 DOI: 10.1093/bib/bbac560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/28/2022] [Accepted: 11/15/2022] [Indexed: 12/23/2022] Open
Abstract
The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledge for guidance. The zipper-like arrangement of 2D and 3D information processing promotes cooperativity between them, and their synergy is the key to our model's success. Experiments on various molecules or conformational datasets including a special newly created chiral molecule dataset comprised of various configurations and conformations demonstrate the superior performance of CFFN. The advantage of CFFN is even more significant in datasets made of small samples. Ablation experiments confirm that fusing 2D and 3D molecular graphs as unambiguous molecular descriptors can not only effectively distinguish molecules and their conformations, but also achieve more accurate and robust prediction of quantum chemical properties.
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Affiliation(s)
- Wenjie Du
- School of Software Engineering, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Xiaoting Yang
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
- School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Di Wu
- School of Software Engineering, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - FenFen Ma
- Suzhou Laboratory , Suzhou 215123, Jiangsu, China
| | - Baicheng Zhang
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Chaochao Bao
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Yaoyuan Huo
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Jun Jiang
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Xin Chen
- Suzhou Laboratory , Suzhou 215123, Jiangsu, China
| | - Yang Wang
- School of Software Engineering, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
- School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
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37
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Tan Z, Li Y, Wu X, Zhang Z, Shi W, Yang S, Zhang W. De novo creation of fluorescent molecules via adversarial generative modeling. RSC Adv 2023; 13:1031-1040. [PMID: 36686951 PMCID: PMC9811934 DOI: 10.1039/d2ra07008a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
The development of AI for fluorescent materials design is technologically demanding due to the issue of accurately forecasting fluorescent properties. Besides the huge efforts made in predicting the photoluminescent properties of organic dyes in terms of machine learning techniques, this article aims to introduce an adversarial generation paradigm for the rational design of fluorescent molecules. Molecular SMILES is employed as the input of a GRU based autoencoder, where the encoding and decoding of the string information are processed. A generative adversarial network is applied on the latent space with a generator to generate samples to mimic the latent space, and a discriminator to distinguish samples from the latent space. It is found that the excited state property distributions of generated molecules fully match those of the original samples, with the molecular synthesizability being accessible as well. Further screening of the generated samples delivers a remarkable luminescence efficiency of molecules epitomized by the significant oscillator strength and charge transfer characteristics, demonstrating the great potential of the adversarial model in enriching the fluorescent library.
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Affiliation(s)
- Zheng Tan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China Chengdu 610054 P. R. China
| | - Yan Li
- Chengdu Polytechnic 83 Tianyi Street Chengdu 610000 P. R. China
| | - Xin Wu
- Xiyuan Quantitative Technology 388 Yizhou Road Chengdu 610000 P. R. China
| | - Ziying Zhang
- Guangzhou Yinfo Information Technology 2 Ruyi Road, Panyu District Guangzhou 511431 P. R. China
| | - Weimei Shi
- Chengdu Polytechnic 83 Tianyi Street Chengdu 610000 P. R. China
| | - Shiqing Yang
- Chengdu Polytechnic 83 Tianyi Street Chengdu 610000 P. R. China
| | - Wanli Zhang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China Chengdu 610054 P. R. China
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38
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Boeije Y, Olivucci M. From a one-mode to a multi-mode understanding of conical intersection mediated ultrafast organic photochemical reactions. Chem Soc Rev 2023; 52:2643-2687. [PMID: 36970950 DOI: 10.1039/d2cs00719c] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
This review discusses how ultrafast organic photochemical reactions are controlled by conical intersections, highlighting that decay to the ground-state at multiple points of the intersection space results in their multi-mode character.
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Affiliation(s)
- Yorrick Boeije
- Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Massimo Olivucci
- Chemistry Department, University of Siena, Via Aldo Moro n. 2, 53100 Siena, Italy
- Chemistry Department, Bowling Green State University, Overman Hall, Bowling Green, Ohio 43403, USA
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39
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Gelin MF, Chen L, Domcke W. Equation-of-Motion Methods for the Calculation of Femtosecond Time-Resolved 4-Wave-Mixing and N-Wave-Mixing Signals. Chem Rev 2022; 122:17339-17396. [PMID: 36278801 DOI: 10.1021/acs.chemrev.2c00329] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Femtosecond nonlinear spectroscopy is the main tool for the time-resolved detection of photophysical and photochemical processes. Since most systems of chemical interest are rather complex, theoretical support is indispensable for the extraction of the intrinsic system dynamics from the detected spectroscopic responses. There exist two alternative theoretical formalisms for the calculation of spectroscopic signals, the nonlinear response-function (NRF) approach and the spectroscopic equation-of-motion (EOM) approach. In the NRF formalism, the system-field interaction is assumed to be sufficiently weak and is treated in lowest-order perturbation theory for each laser pulse interacting with the sample. The conceptual alternative to the NRF method is the extraction of the spectroscopic signals from the solutions of quantum mechanical, semiclassical, or quasiclassical EOMs which govern the time evolution of the material system interacting with the radiation field of the laser pulses. The NRF formalism and its applications to a broad range of material systems and spectroscopic signals have been comprehensively reviewed in the literature. This article provides a detailed review of the suite of EOM methods, including applications to 4-wave-mixing and N-wave-mixing signals detected with weak or strong fields. Under certain circumstances, the spectroscopic EOM methods may be more efficient than the NRF method for the computation of various nonlinear spectroscopic signals.
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Affiliation(s)
- Maxim F Gelin
- School of Science, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Lipeng Chen
- Max-Planck-Institut für Physik komplexer Systeme, Nöthnitzer Strasse 38, D-01187 Dresden, Germany
| | - Wolfgang Domcke
- Department of Chemistry, Technical University of Munich, D-85747 Garching,Germany
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40
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Lin K, Peng J, Xu C, Gu FL, Lan Z. Trajectory Propagation of Symmetrical Quasi-classical Dynamics with Meyer-Miller Mapping Hamiltonian Using Machine Learning. J Phys Chem Lett 2022; 13:11678-11688. [PMID: 36511563 DOI: 10.1021/acs.jpclett.2c02159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The long short-term memory recurrent neural network (LSTM-RNN) approach is applied to realize the trajectory-based nonadiabatic dynamics within the framework of the symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian (MM-SQC). After construction, the LSTM-RNN model allows us to propagate the entire trajectory evolutions of all involved degrees of freedoms (DOFs) from initial conditions. The proposed idea is proven to be reliable and accurate in the simulations of the dynamics of several site-exciton electron-phonon coupling models and three Tully's scattering models. It indicates that the LSTM-RNN model perfectly captures the dynamical information on the trajectory evolution in the MM-SQC dynamics. Our work proposes a novel machine learning approach in the simulation of trajectory-based nonadiabatic dynamic of complex systems with a large number of DOFs.
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Affiliation(s)
- Kunni Lin
- School of Chemistry, South China Normal University, Guangzhou 510006, P. R. China
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Jiawei Peng
- School of Chemistry, South China Normal University, Guangzhou 510006, P. R. China
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Feng Long Gu
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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41
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Abstract
Chemiluminescence (CL) utilizing chemiexcitation for energy transformation is one of the most highly sensitive and useful analytical techniques. The chemiexcitation is a chemical process of a ground-state reactant producing an excited-state product, in which a nonadiabatic event is facilitated by conical intersections (CIs), the specific molecular geometries where electronic states are degenerated. Cyclic peroxides, especially 1,2-dioxetane/dioxetanone derivatives, are the iconic chemiluminescent substances. In this Perspective, we concentrated on the CIs in the CL of cyclic peroxides. We first present a computational overview on the role of CIs between the ground (S0) state and the lowest singlet excited (S1) state in the thermolysis of cyclic peroxides. Subsequently, we discuss the role of the S0/S1 CI in the CL efficiency and point out misunderstandings in some theoretical studies on the singlet chemiexcitations of cyclic peroxides. Finally, we address the challenges and future prospects in theoretically calculating S0/S1 CIs and simulating the dynamics and chemiexcitation efficiency in the CL of cyclic peroxides.
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Affiliation(s)
- Ling Yue
- Key Laboratory for Non-equilibrium Synthesis and Modulation of Condensed Matter, Ministry of Education, School of Chemistry, Xi'an Jiaotong University, Xi'an, Shaanxi710049, China
| | - Ya-Jun Liu
- Center for Advanced Materials Research, Beijing Normal University, Zhuhai519087, China
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing100875, China
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42
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Zhang Y, Lin Q, Jiang B. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Yaolong Zhang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Qidong Lin
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Bin Jiang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
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43
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Barbatti M, Bondanza M, Crespo-Otero R, Demoulin B, Dral PO, Granucci G, Kossoski F, Lischka H, Mennucci B, Mukherjee S, Pederzoli M, Persico M, Pinheiro Jr M, Pittner J, Plasser F, Sangiogo Gil E, Stojanovic L. Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles. J Chem Theory Comput 2022; 18:6851-6865. [PMID: 36194696 PMCID: PMC9648185 DOI: 10.1021/acs.jctc.2c00804] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Indexed: 12/01/2022]
Abstract
Newton-X is an open-source computational platform to perform nonadiabatic molecular dynamics based on surface hopping and spectrum simulations using the nuclear ensemble approach. Both are among the most common methodologies in computational chemistry for photophysical and photochemical investigations. This paper describes the main features of these methods and how they are implemented in Newton-X. It emphasizes the newest developments, including zero-point-energy leakage correction, dynamics on complex-valued potential energy surfaces, dynamics induced by incoherent light, dynamics based on machine-learning potentials, exciton dynamics of multiple chromophores, and supervised and unsupervised machine learning techniques. Newton-X is interfaced with several third-party quantum-chemistry programs, spanning a broad spectrum of electronic structure methods.
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Affiliation(s)
- Mario Barbatti
- Aix
Marseille University, CNRS, ICR, 13013Marseille, France
- Institut
Universitaire de France, 75231Paris, France
| | - Mattia Bondanza
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi
13, 56124Pisa, Italy
| | - Rachel Crespo-Otero
- Department
of Chemistry, Queen Mary University of London, Mile End Road, E1 4NSLondon, U.K.
| | | | - Pavlo O. Dral
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial
Key Laboratory of Theoretical and Computational Chemistry, Department
of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, 361005Xiamen, China
| | - Giovanni Granucci
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi
13, 56124Pisa, Italy
| | - Fábris Kossoski
- Laboratoire
de Chimie et Physique Quantiques (UMR 5626), Université de Toulouse, CNRS, UPS, 31000Toulouse, France
| | - Hans Lischka
- Department
of Chemistry and Biochemistry, Texas Tech
University, Lubbock, Texas79409, United States
| | - Benedetta Mennucci
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi
13, 56124Pisa, Italy
| | | | - Marek Pederzoli
- J.
Heyrovsky Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, v.v.i., Dolejškova 3, 18223Prague 8, Czech Republic
| | - Maurizio Persico
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi
13, 56124Pisa, Italy
| | | | - Jiří Pittner
- J.
Heyrovsky Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, v.v.i., Dolejškova 3, 18223Prague 8, Czech Republic
| | - Felix Plasser
- Department
of Chemistry, Loughborough University, LE11 3TULoughborough, U.K.
| | - Eduarda Sangiogo Gil
- Dipartimento
di Chimica e Chimica Industriale, Università
di Pisa, via Moruzzi
13, 56124Pisa, Italy
| | - Ljiljana Stojanovic
- Department
of Physics and Astronomy, University College
London, Gower Street, WC1E 6BTLondon, U.K.
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44
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Krenn M, Ai Q, Barthel S, Carson N, Frei A, Frey NC, Friederich P, Gaudin T, Gayle AA, Jablonka KM, Lameiro RF, Lemm D, Lo A, Moosavi SM, Nápoles-Duarte JM, Nigam A, Pollice R, Rajan K, Schatzschneider U, Schwaller P, Skreta M, Smit B, Strieth-Kalthoff F, Sun C, Tom G, Falk von Rudorff G, Wang A, White AD, Young A, Yu R, Aspuru-Guzik A. SELFIES and the future of molecular string representations. PATTERNS (NEW YORK, N.Y.) 2022; 3:100588. [PMID: 36277819 PMCID: PMC9583042 DOI: 10.1016/j.patter.2022.100588] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
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Affiliation(s)
- Mario Krenn
- Max Planck Institute for the Science of Light (MPL), Erlangen, Germany
| | - Qianxiang Ai
- Department of Chemistry, Fordham University, The Bronx, NY, USA
| | - Senja Barthel
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nessa Carson
- Syngenta Jealott’s Hill International Research Centre, Bracknell, Berkshire, UK
| | - Angelo Frei
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, UK
| | - Nathan C. Frey
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pascal Friederich
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Théophile Gaudin
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- IBM Research Europe, Zürich, Switzerland
| | | | - Kevin Maik Jablonka
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
| | - Rafael F. Lameiro
- Medicinal and Biological Chemistry Group, São Carlos Institute of Chemistry, University of São Paulo, São Paulo, Brazil
| | - Dominik Lemm
- Faculty of Physics, University of Vienna, Vienna, Austria
| | - Alston Lo
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Seyed Mohamad Moosavi
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | | | - AkshatKumar Nigam
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Robert Pollice
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller Universität Jena, Jena, Germany
| | - Ulrich Schatzschneider
- Institut für Anorganische Chemie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Philippe Schwaller
- IBM Research Europe, Zürich, Switzerland
- Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marta Skreta
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
| | - Felix Strieth-Kalthoff
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Chong Sun
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Gary Tom
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | | | - Andrew Wang
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Solar Fuels Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Andrew D. White
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
| | - Adamo Young
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Materials Science, University of Toronto, Toronto, ON, Canada
- Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, ON, Canada
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45
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King D, Hermes MR, Truhlar DG, Gagliardi L. Large-Scale Benchmarking of Multireference Vertical-Excitation Calculations via Automated Active-Space Selection. J Chem Theory Comput 2022; 18:6065-6076. [PMID: 36112354 PMCID: PMC9558375 DOI: 10.1021/acs.jctc.2c00630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Indexed: 11/29/2022]
Abstract
We have calculated state-averaged complete-active-space self-consistent-field (SA-CASSCF), multiconfiguration pair-density functional theory (MC-PDFT), hybrid MC-PDFT (HMC-PDFT), and n-electron valence state second-order perturbation theory (NEVPT2) excitation energies with the approximate pair coefficient (APC) automated active-space selection scheme for the QUESTDB benchmark database of 542 vertical excitation energies. We eliminated poor active spaces (20-40% of calculations) by applying a threshold to the SA-CASSCF absolute error. With the remaining calculations, we find that NEVPT2 performance is significantly impacted by the size of the basis set the wave functions are converged in, regardless of the quality of their description, which is a problem absent in MC-PDFT. Additionally, we find that HMC-PDFT is a significant improvement over MC-PDFT with the translated PBE (tPBE) density functional and that it performs about as well as NEVPT2 and second-order coupled cluster on a set of 373 excitations in the QUESTDB database. We optimized the percentage of SA-CASSCF energy to include in HMC-PDFT when using the tPBE on-top functional, and we find the 25% value used in tPBE0 to be optimal. This work is by far the largest benchmarking of MC-PDFT and HMC-PDFT to date, and the data produced in this work are useful as a validation of HMC-PDFT and of the APC active-space selection scheme. We have made all the wave functions produced in this work (orbitals and CI vectors) available to the public and encourage the community to utilize this data as a tool in the development of further multireference model chemistries.
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Affiliation(s)
- Daniel
S. King
- Department
of Chemistry, University of Chicago, Chicago Illinois 60637, United States
| | - Matthew R. Hermes
- Department
of Chemistry, University of Chicago, Chicago Illinois 60637, United States
| | - Donald G. Truhlar
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputng
Institute, University of Minnesota, Minneapolis Minnesota 55455-0431, United States
| | - Laura Gagliardi
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago Illinois 60637, United States
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46
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Lin K, Peng J, Xu C, Gu FL, Lan Z. Automatic Evolution of Machine-Learning-Based Quantum Dynamics with Uncertainty Analysis. J Chem Theory Comput 2022; 18:5837-5855. [DOI: 10.1021/acs.jctc.2c00702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Kunni Lin
- School of Chemistry, South China Normal University, Guangzhou510006, P. R. China
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou510006, P. R. China
| | - Jiawei Peng
- School of Chemistry, South China Normal University, Guangzhou510006, P. R. China
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou510006, P. R. China
| | - Chao Xu
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou510006, P. R. China
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou510006, P. R. China
| | - Feng Long Gu
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou510006, P. R. China
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou510006, P. R. China
| | - Zhenggang Lan
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou510006, P. R. China
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou510006, P. R. China
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47
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Morzan UN, Díaz Mirón G, Grisanti L, González Lebrero MC, Kaminski Schierle GS, Hassanali A. Non-Aromatic Fluorescence in Biological Matter: The Exception or the Rule? J Phys Chem B 2022; 126:7203-7211. [PMID: 36128666 DOI: 10.1021/acs.jpcb.2c04280] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
While in the vast majority of cases fluorescence in biological matter has been attributed to aromatic or conjugated groups, peptides associated with neurodegenerative diseases, such as Alzheimer's, Parkinson's, or Huntington's, have been recently shown to display an intrinsic visible fluorescence even in the absence of aromatic residues. This has called the attention of researchers from many different fields, trying to understand the origin of this peculiar behavior and, at the same time, motivating the search for novel strategies to control the optical properties of new biophotonic materials. Today, after nearly 15 years of its discovery, there is a growing consensus about the mechanism underlying this phenomenon, namely, that electronic interactions between non-optically active molecules can result in supramolecular assemblies that are fluorescent. Despite this progress, many aspects of this phenomenon remain uncharted territory. In this Perspective, we lay down the state-of-the-art in the field highlighting the open questions from both experimental and theoretical fronts in this fascinating emerging area of non-aromatic fluorescence.
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Affiliation(s)
- Uriel N Morzan
- International Centre for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy
| | - Gonzalo Díaz Mirón
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Inorgánica, Analítica y Química Física, Universidad de Buenos Aires, C1053 Buenos Aires, Argentina
| | - Luca Grisanti
- Division of Theoretical Physics, Ruđer Bos̆cković Institute, Bijenic̆ka cesta 54, 10000 Zagreb, Croatia
| | - Mariano C González Lebrero
- Facultad de Ciencias Exactas y Naturales, Departamento de Química Inorgánica, Analítica y Química Física, Universidad de Buenos Aires, C1053 Buenos Aires, Argentina
| | | | - Ali Hassanali
- International Centre for Theoretical Physics, Strada Costiera 11, 34151 Trieste, Italy
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48
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Fedik N, Zubatyuk R, Kulichenko M, Lubbers N, Smith JS, Nebgen B, Messerly R, Li YW, Boldyrev AI, Barros K, Isayev O, Tretiak S. Extending machine learning beyond interatomic potentials for predicting molecular properties. Nat Rev Chem 2022; 6:653-672. [PMID: 37117713 DOI: 10.1038/s41570-022-00416-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 11/09/2022]
Abstract
Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.
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49
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Bosch D, Wang J, Blancafort L. Fingerprint-based deep neural networks can model thermodynamic and optical properties of eumelanin DHI dimers. Chem Sci 2022; 13:8942-8946. [PMID: 36091209 PMCID: PMC9365084 DOI: 10.1039/d2sc02461f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/03/2022] [Indexed: 11/21/2022] Open
Abstract
Eumelanin is the biopolymer responsible for photoprotection in living beings and holds great promise as a smart biomaterial, but its detailed structure has not been characterized experimentally. Theoretical models are urgently needed to improve our knowledge of eumelanin's function and exploit its properties, but the enormous amount of possible oligomer components has made modelling not possible until now. Here we show that the stability and lowest vertical optical absorption of 5,6-dihydroxyindole (DHI) eumelanin dimer components can be modeled with deep neural networks, using fingerprint-like molecular representations as input. In spite of the modest data set size, average errors of only 6 and 9% for stability and S1 absorption energy are obtained. Our fingerprints code the connectivity and oxidation patterns of the dimers in a straightforward, unambiguous way and can be extended to larger oligomers. This proof-of-principle work shows that machine learning can be applied to help solve the structural challenge of melanin.
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Affiliation(s)
- Daniel Bosch
- Departament de Química, Institut de Química Computacional i Catàlisi, Universitat de Girona. Facultat de Ciències C/M. A. Capmany 69 17003 Girona Spain
| | - Jun Wang
- Jiangsu Key Laboratory for Chemistry of Low-Dimensional Materials, Jiangsu Engineering Laboratory for Environment Functional Materials, Huaiyin Normal University No. 111 West Changjiang Road Huaian 223300 Jiangsu P. R. China
| | - Lluís Blancafort
- Departament de Química, Institut de Química Computacional i Catàlisi, Universitat de Girona. Facultat de Ciències C/M. A. Capmany 69 17003 Girona Spain
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50
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Golze D, Hirvensalo M, Hernández-León P, Aarva A, Etula J, Susi T, Rinke P, Laurila T, Caro MA. Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2022; 34:6240-6254. [PMID: 35910537 PMCID: PMC9330771 DOI: 10.1021/acs.chemmater.1c04279] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 06/30/2022] [Indexed: 06/15/2023]
Abstract
We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which X-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with GW and uses kernel ridge regression for the ML predictions. We apply the new approach to disordered materials and small molecules containing carbon, hydrogen, and oxygen and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server.
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Affiliation(s)
- Dorothea Golze
- Faculty
of Chemistry and Food Chemistry, Technische
Universität Dresden, 01062 Dresden, Germany
- Department
of Applied Physics, Aalto University, 02150 Espoo, Finland
| | - Markus Hirvensalo
- Department
of Applied Physics, Aalto University, 02150 Espoo, Finland
| | | | - Anja Aarva
- Department
of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
| | - Jarkko Etula
- Department
of Chemistry and Materials Science, Aalto
University, 02150 Espoo, Finland
| | - Toma Susi
- University
of Vienna, Faculty of Physics, Boltzmanngasse 5, 1090 Vienna, Austria
| | - Patrick Rinke
- Department
of Applied Physics, Aalto University, 02150 Espoo, Finland
| | - Tomi Laurila
- Department
of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
- Department
of Chemistry and Materials Science, Aalto
University, 02150 Espoo, Finland
| | - Miguel A. Caro
- Department
of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
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