51
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Rollins N, Pugh SL, Maley SM, Grant BO, Hamilton RS, Teynor MS, Carlsen R, Jenkins JR, Ess DH. Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene. J Phys Chem A 2020; 124:4813-4826. [PMID: 32412755 DOI: 10.1021/acs.jpca.9b10410] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Experimentally, the thermal gas-phase deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene (1) results in the loss of N2 and the formation of bicyclo products 3 (exo) and 4 (endo) in a nonstatistical ratio, with preference for the exo product. Here, we report unrestricted M06-2X quasiclassical trajectories initialized from the concerted N2 ejection transition state that were able to replicate the experimental preference to form 3. We found that the 3:4 ratio results from the relative amounts of very fast (ballistic) exotype trajectories versus trajectories that lead to the 1,3-diradical intermediate 2. These quasiclassical trajectories provided a set of transition-state vibrational, velocity, momenta, and geometric features for the machine learning analysis. A selection of popular supervised classification algorithms (e.g., random forest) provided poor prediction of trajectory outcomes based on only transition-state vibrational quanta and energy features. However, these machine learning models provided more accurate predictions using atomic velocities and atomic positions, attaining ∼70% accuracy using initial conditions and between 85 and 95% accuracy at later reaction time steps. This increased accuracy allowed the feature importance analysis to reveal that, at the later-time analysis, the methylene bridge out-of-plane bending is correlated with trajectory outcomes for the formation of either the exo product or toward the diradical intermediate. Possible reasons for the struggle of machine learning algorithms to classify trajectories based on transition-state features is the heavily overlapping feature values, the finite but very large possible vibrational mode combinations, and the possibility of chaos as trajectories propagate. We examined this chaos by comparing a set of nearly identical trajectories that differed by only a very small scaling of the kinetic energies resulting from the transition-state reaction coordinate.
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Affiliation(s)
- Nick Rollins
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel L Pugh
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Steven M Maley
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Benjamin O Grant
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - R Spencer Hamilton
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Matthew S Teynor
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Ryan Carlsen
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Jordan R Jenkins
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
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52
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Aquilante F, Autschbach J, Baiardi A, Battaglia S, Borin VA, Chibotaru LF, Conti I, De Vico L, Delcey M, Fdez Galván I, Ferré N, Freitag L, Garavelli M, Gong X, Knecht S, Larsson ED, Lindh R, Lundberg M, Malmqvist PÅ, Nenov A, Norell J, Odelius M, Olivucci M, Pedersen TB, Pedraza-González L, Phung QM, Pierloot K, Reiher M, Schapiro I, Segarra-Martí J, Segatta F, Seijo L, Sen S, Sergentu DC, Stein CJ, Ungur L, Vacher M, Valentini A, Veryazov V. Modern quantum chemistry with [Open]Molcas. J Chem Phys 2020; 152:214117. [PMID: 32505150 DOI: 10.1063/5.0004835] [Citation(s) in RCA: 226] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOLCAS/OpenMolcas is an ab initio electronic structure program providing a large set of computational methods from Hartree-Fock and density functional theory to various implementations of multiconfigurational theory. This article provides a comprehensive overview of the main features of the code, specifically reviewing the use of the code in previously reported chemical applications as well as more recent applications including the calculation of magnetic properties from optimized density matrix renormalization group wave functions.
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Affiliation(s)
- Francesco Aquilante
- Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Jochen Autschbach
- Department of Chemistry, University at Buffalo, Buffalo, New York 14260-3000, USA
| | - Alberto Baiardi
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Stefano Battaglia
- Department of Chemistry - BMC, Uppsala University, P.O. Box 576, SE-751 23 Uppsala, Sweden
| | - Veniamin A Borin
- Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Liviu F Chibotaru
- Department of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Irene Conti
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Luca De Vico
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
| | - Mickaël Delcey
- Department of Chemistry - Ångström Laboratory, Uppsala University, SE-751 21 Uppsala, Sweden
| | - Ignacio Fdez Galván
- Department of Chemistry - BMC, Uppsala University, P.O. Box 576, SE-751 23 Uppsala, Sweden
| | - Nicolas Ferré
- Aix-Marseille University, CNRS, Institut Chimie Radicalaire, Marseille, France
| | - Leon Freitag
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Marco Garavelli
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Xuejun Gong
- Department of Chemistry, University of Singapore, 3 Science Drive 3, 117543 Singapore
| | - Stefan Knecht
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Ernst D Larsson
- Division of Theoretical Chemistry, Lund University, P.O. Box 124, Lund 22100, Sweden
| | - Roland Lindh
- Department of Chemistry - BMC, Uppsala University, P.O. Box 576, SE-751 23 Uppsala, Sweden
| | - Marcus Lundberg
- Department of Chemistry - Ångström Laboratory, Uppsala University, SE-751 21 Uppsala, Sweden
| | - Per Åke Malmqvist
- Division of Theoretical Chemistry, Lund University, P.O. Box 124, Lund 22100, Sweden
| | - Artur Nenov
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Jesper Norell
- Department of Physics, AlbaNova University Center, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Michael Odelius
- Department of Physics, AlbaNova University Center, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Massimo Olivucci
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
| | - Thomas B Pedersen
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, N-0315 Oslo, Norway
| | - Laura Pedraza-González
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
| | - Quan M Phung
- Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Chikusa, Nagoya 464-8602, Japan
| | - Kristine Pierloot
- Department of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Igor Schapiro
- Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Javier Segarra-Martí
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, 80 Wood Lane, London W12 0BZ, United Kingdom
| | - Francesco Segatta
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Luis Seijo
- Departamento de Química, Instituto Universitario de Ciencia de Materiales Nicolás Cabrera, and Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Saumik Sen
- Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | | | - Christopher J Stein
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Liviu Ungur
- Department of Chemistry, University of Singapore, 3 Science Drive 3, 117543 Singapore
| | - Morgane Vacher
- Laboratoire CEISAM - UMR CNRS 6230, Université de Nantes, 44300 Nantes, France
| | - Alessio Valentini
- Theoretical Physical Chemistry, Research Unit MolSys, Université de Liège, Allée du 6 Août, 11, 4000 Liège, Belgium
| | - Valera Veryazov
- Division of Theoretical Chemistry, Lund University, P.O. Box 124, Lund 22100, Sweden
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Yue L, Yu L, Xu C, Zhu C, Liu Y. Quantum yields of singlet and triplet chemiexcitation of dimethyl 1,2-dioxetane: ab initio nonadiabatic molecular dynamic simulations. Phys Chem Chem Phys 2020; 22:11440-11451. [PMID: 32390027 DOI: 10.1039/d0cp00811g] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The global nonadiabatic switching on-the-fly trajectory surface hopping simulation at the 8SA-CASSCF quantum level has been performed to estimate the quantum yield of chemiexcitation for the uncatalyzed decomposition reaction of the open-shell biradical trans-3,4-dimethyl-1,2-dioxetane system. The present ab initio nonadiabatic molecular dynamic simulation involving both conical intersection and intersystem crossing is to compute for the first time the population evolution of quantum yields at the four lowest singlet and four lowest triplet states. The simulated results demonstrate not only the stepwise dissociation of O-O and C-C bond breaking, but also confirm the existence of a biradical entropic trap which is responsible for chemiexcitation. The simulated quantum yield of the triplet chemiexcitation ΦT1 = 0.266 ± 0.096 agrees with the experimental value of 0.20 ± 0.04 very well. The present nonadiabatic molecular dynamic simulation of dimethyl 1,2-dioxetanes provides a further advanced understanding and stepping stone for future studies on chemi- and bio-luminescence.
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Affiliation(s)
- Ling Yue
- Key Laboratory for Non-Equilibrium Synthesis and Modulation of Condensed Matter, Ministry of Education, School of Sciences, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China and Department of Applied Chemistry and Institute of Molecular Science, National Chiao-Tung University, Hsinchu 30010, Taiwan.
| | - Le Yu
- Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710069, China
| | - Chao Xu
- Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education, School of Chemistry & Environment of South China Normal University, Guangzhou 51006, P. R. China
| | - Chaoyuan Zhu
- Department of Applied Chemistry and Institute of Molecular Science, National Chiao-Tung University, Hsinchu 30010, Taiwan. and Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education, School of Chemistry & Environment of South China Normal University, Guangzhou 51006, P. R. China and Center for Emergent Functional Matter Science, National Chiao Tung University, Hsinchu 30010, Taiwan
| | - Yajun Liu
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
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54
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Yu H, Wang Y, Wang X, Zhang J, Ye S, Huang Y, Luo Y, Sharman E, Chen S, Jiang J. Using Machine Learning to Predict the Dissociation Energy of Organic Carbonyls. J Phys Chem A 2020; 124:3844-3850. [PMID: 32315178 DOI: 10.1021/acs.jpca.0c01280] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Bond dissociation energy (BDE), an indicator of the strength of chemical bonds, exhibits great potential for evaluating and screening high-performance materials and catalysts, which are of critical importance in industrial applications. However, the measurement or computation of BDE via conventional experimental or theoretical methods is usually costly and involved, substantially preventing the BDE from being applied to large-scale and high-throughput studies. Therefore, a potentially more efficient approach for estimating BDE is highly desirable. To this end, we combined first-principles calculations and machine learning techniques, including neural networks and random forest, to explore the inner relationships between carbonyl structure and its BDE. Results show that machine learning can not only effectively reproduce the computed BDEs of carbonyls but also in turn serve as guidance for the rational design of carbonyl structure aimed at optimizing performance.
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Affiliation(s)
- Haishan Yu
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Ying Wang
- Key Laboratory of Cluster Science of Ministry of Education, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Xijun Wang
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh 27606, North Carolina, United States
| | - Jinxiao Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Sheng Ye
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Yan Huang
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Yi Luo
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China
| | - Edward Sharman
- Department of Neurology, University of California, Irvine 92697, California, United States
| | - Shilu Chen
- Key Laboratory of Cluster Science of Ministry of Education, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Jun Jiang
- Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, Anhui, China
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55
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Chen CH, Melo MC, Berglund N, Khan A, de la Fuente-Nunez C, Ulmschneider JP, Ulmschneider MB. Understanding and modelling the interactions of peptides with membranes: from partitioning to self-assembly. Curr Opin Struct Biol 2020; 61:160-166. [PMID: 32006812 DOI: 10.1016/j.sbi.2019.12.021] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 12/27/2019] [Accepted: 12/28/2019] [Indexed: 12/14/2022]
Abstract
Atomic detail simulations are starting to reveal how flexible polypeptides interact with fluid lipid bilayers. These insights are transforming our understanding of one of the fundamental processes in biology: membrane protein folding and assembly. Advanced molecular dynamics (MD) simulation techniques enable accurate prediction of protein structure, folding pathways and assembly in microsecond-timescales. Such simulations show how membrane-active peptides self-assemble in cell membranes, revealing their binding, folding, insertion, and aggregation, while at the same time providing atomic resolution details of peptide-lipid interactions. Essential to the impact of simulations are experimental approaches that enable calibration and validation of the computational models and techniques. In this review, we summarize the current development of applying unbiased atomic detail MD simulations and the relation to experimental techniques, to study peptide folding and provide our perspective of the field.
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Affiliation(s)
- Charles H Chen
- Department of Chemistry, King's College London, London, UK
| | - Marcelo Cr Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, Penn Institute for Computational Science, and Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nils Berglund
- Department of Chemistry, Aarhus University, Aarhus, Denmark
| | - Ayesha Khan
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, Penn Institute for Computational Science, and Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| | - Jakob P Ulmschneider
- Institute of Natural Sciences and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China.
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56
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Abstract
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.
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Affiliation(s)
- 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, Xiamen 361005, China
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57
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Kammeraad JA, Goetz J, Walker EA, Tewari A, Zimmerman PM. What Does the Machine Learn? Knowledge Representations of Chemical Reactivity. J Chem Inf Model 2020; 60:1290-1301. [PMID: 32091880 DOI: 10.1021/acs.jcim.9b00721] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In a departure from conventional chemical approaches, data-driven models of chemical reactions have recently been shown to be statistically successful using machine learning. These models, however, are largely black box in character and have not provided the kind of chemical insights that historically advanced the field of chemistry. To examine the knowledgebase of machine-learning models-what does the machine learn-this article deconstructs black-box machine-learning models of a diverse chemical reaction data set. Through experimentation with chemical representations and modeling techniques, the analysis provides insights into the nature of how statistical accuracy can arise, even when the model lacks informative physical principles. By peeling back the layers of these complicated models we arrive at a minimal, chemically intuitive model (and no machine learning involved). This model is based on systematic reaction-type classification and Evans-Polanyi relationships within reaction types which are easily visualized and interpreted. Through exploring this simple model, we gain deeper understanding of the data set and uncover a means for expert interactions to improve the model's reliability.
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Affiliation(s)
- Joshua A Kammeraad
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Jack Goetz
- Department of Statistics, University of Michigan, 1085 South University Avenue, Ann Arbor, Michigan 48109, United States
| | - Eric A Walker
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Ambuj Tewari
- Department of Statistics, University of Michigan, 1085 South University Avenue, Ann Arbor, Michigan 48109, United States
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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58
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Jeong W, Stoneburner SJ, King D, Li R, Walker A, Lindh R, Gagliardi L. Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation. J Chem Theory Comput 2020; 16:2389-2399. [DOI: 10.1021/acs.jctc.9b01297] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- WooSeok Jeong
- Department of Chemistry, Nanoporous Materials Genome Center, Minnesota Supercomputing Institute, and Chemical Theory Center, University of Minnesota, 207 Pleasant Street Southeast, Minneapolis, Minnesota 55455, United States
| | - Samuel J. Stoneburner
- Department of Chemistry, Nanoporous Materials Genome Center, Minnesota Supercomputing Institute, and Chemical Theory Center, University of Minnesota, 207 Pleasant Street Southeast, Minneapolis, Minnesota 55455, United States
| | - Daniel King
- Department of Chemistry, Nanoporous Materials Genome Center, Minnesota Supercomputing Institute, and Chemical Theory Center, University of Minnesota, 207 Pleasant Street Southeast, Minneapolis, Minnesota 55455, United States
| | - Ruye Li
- Department of Chemistry, Nanoporous Materials Genome Center, Minnesota Supercomputing Institute, and Chemical Theory Center, University of Minnesota, 207 Pleasant Street Southeast, Minneapolis, Minnesota 55455, United States
| | - Andrew Walker
- Department of Computer Science and Engineering, University of Minnesota, 200 Union Street Southeast, Minneapolis, Minnesota 55455, United States
| | - Roland Lindh
- Department of Chemistry—BMC, and Uppsala Center for Computational Chemistry—UC3, Uppsala University, 751 23 Uppsala, Sweden
| | - Laura Gagliardi
- Department of Chemistry, Nanoporous Materials Genome Center, Minnesota Supercomputing Institute, and Chemical Theory Center, University of Minnesota, 207 Pleasant Street Southeast, Minneapolis, Minnesota 55455, United States
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59
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Van Lommel R, Zhao J, De Borggraeve WM, De Proft F, Alonso M. Molecular dynamics based descriptors for predicting supramolecular gelation. Chem Sci 2020. [DOI: 10.1039/d0sc00129e] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Four molecular dynamics-based descriptors were derived able to classify gelator–solvent combinations as a gel, precipitate or clear solution.
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Affiliation(s)
- Ruben Van Lommel
- Molecular Design and Synthesis
- Department of Chemistry
- KU Leuven
- 3001 Leuven
- Belgium
| | - Jianyu Zhao
- Eenheid Algemene Chemie (ALGC)
- Vrije Universiteit Brussel (VUB)
- 1050 Brussels
- Belgium
| | - Wim M. De Borggraeve
- Molecular Design and Synthesis
- Department of Chemistry
- KU Leuven
- 3001 Leuven
- Belgium
| | - Frank De Proft
- Eenheid Algemene Chemie (ALGC)
- Vrije Universiteit Brussel (VUB)
- 1050 Brussels
- Belgium
| | - Mercedes Alonso
- Eenheid Algemene Chemie (ALGC)
- Vrije Universiteit Brussel (VUB)
- 1050 Brussels
- Belgium
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60
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Abstract
There has been an upsurge of interest in applying machine learning to chemistry, and impressive predictive accuracies have been achieved, but this has been done without providing any insight into what has been learnt from the training data.
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61
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Cova TFGG, Pais AACC. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns. Front Chem 2019; 7:809. [PMID: 32039134 PMCID: PMC6988795 DOI: 10.3389/fchem.2019.00809] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 11/11/2019] [Indexed: 12/14/2022] Open
Abstract
Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.
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Affiliation(s)
- Tânia F. G. G. Cova
- Coimbra Chemistry Centre, CQC, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Alberto A. C. C. Pais
- Coimbra Chemistry Centre, CQC, Department of Chemistry, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
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Alvarez-Ramírez F, Ruiz-Morales Y. Database of Nuclear Independent Chemical Shifts (NICS) versus NICSZZ of Polycyclic Aromatic Hydrocarbons (PAHs). J Chem Inf Model 2019; 60:611-620. [DOI: 10.1021/acs.jcim.9b00909] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Fernando Alvarez-Ramírez
- Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas Norte 152, Mexico City 07730, Mexico
| | - Yosadara Ruiz-Morales
- Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas Norte 152, Mexico City 07730, Mexico
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63
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Westermayr J, Gastegger M, Menger MFSJ, Mai S, González L, Marquetand P. Machine learning enables long time scale molecular photodynamics simulations. Chem Sci 2019; 10:8100-8107. [PMID: 31857878 PMCID: PMC6849489 DOI: 10.1039/c9sc01742a] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 08/02/2019] [Indexed: 02/04/2023] Open
Abstract
Photo-induced processes are fundamental in nature but accurate simulations of their dynamics are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy.
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Affiliation(s)
- Julia Westermayr
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
| | - Michael Gastegger
- Machine Learning Group , Technical University of Berlin , 10587 Berlin , Germany
| | - Maximilian F S J Menger
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
- Dipartimento di Chimica e Chimica Industriale , University of Pisa , Via G. Moruzzi 13 , 56124 Pisa , Italy
| | - Sebastian Mai
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
| | - Leticia González
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
| | - Philipp Marquetand
- Institute of Theoretical Chemistry , Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria .
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Grazioli G, Roy S, Butts CT. Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines. J Chem Inf Model 2019; 59:2753-2764. [DOI: 10.1021/acs.jcim.9b00134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Gianmarc Grazioli
- California Institute for Telecommunications and Information Technology, University of California, Irvine, California 92697, United States
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Saswata Roy
- Department of Chemistry, University of California, Irvine, California 92697, United States
| | - Carter T. Butts
- California Institute for Telecommunications and Information Technology, University of California, Irvine, California 92697, United States
- Departments of Sociology, Statistics, and EECS, University of California, Irvine, California 92697, United States
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The Unreasonable Effectiveness of Equations: Advanced Modeling For Biopharmaceutical Process Development. COMPUTER AIDED CHEMICAL ENGINEERING 2019. [DOI: 10.1016/b978-0-12-818597-1.50023-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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