1
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Wang Y, Lin Z, Ouyang R, Jiang B, Zhang IY, Xu X. Toward Efficient and Unified Treatment of Static and Dynamic Correlations in Generalized Kohn-Sham Density Functional Theory. JACS AU 2024; 4:3205-3216. [PMID: 39211596 PMCID: PMC11350721 DOI: 10.1021/jacsau.4c00488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/26/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
Accurate description of the static correlation poses a persistent challenge in electronic structure theory, particularly when it has to be concurrently considered with the dynamic correlation. We develop here a method in the generalized Kohn-Sham density functional theory (DFT) framework, named R-xDH7-SCC15, which achieves an unprecedented accuracy in capturing the static correlation, while maintaining a good description of the dynamic correlation on par with the state-of-the-art DFT and wave function theory methods, all grounded in the same single-reference black-box methodology. Central to R-xDH7-SCC15 is a general-purpose static correlation correction (SCC) model applied to the renormalized XYG3-type doubly hybrid method (R-xDH7). The SCC model development involves a hybrid machine learning strategy that integrates symbolic regression with nonlinear parameter optimization, aiming to achieve a balance between generalization capability, numerical accuracy, and interpretability. Extensive benchmark studies confirm the robustness and broad applicability of R-xDH7-SCC15 across a diverse array of main-group chemical scenarios. Notably, it displays exceptional aptitude in accurately characterizing intricate reaction kinetics and dynamic processes in regions distant from equilibrium, where the influence of static correlation is most profound. Its capability to consistently and efficiently predict the whole energy profiles, activation barriers, and reaction pathways within a user-friendly "black-box" framework represents an important advance in the field of electronic structure theory.
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Affiliation(s)
- Yizhen Wang
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
| | - Zihan Lin
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
| | - Runhai Ouyang
- Materials
Genome Institute, Shanghai University, Shanghai 200444, China
| | - Bin Jiang
- Key
Laboratory of Precision and Intelligent Chemistry, Department of Chemical
Physics, University of Science and Technology
of China, Hefei, Anhui 230026, China
- Hefei
National Laboratory, Hefei 230088, China
| | - Igor Ying Zhang
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
- Hefei
National Laboratory, Hefei 230088, China
- Shanghai
Key Laboratory of Bioactive Small Molecules, Shanghai200032, China
| | - Xin Xu
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
- Hefei
National Laboratory, Hefei 230088, China
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2
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Voss J. Machine learning for accuracy in density functional approximations. J Comput Chem 2024; 45:1829-1845. [PMID: 38668453 DOI: 10.1002/jcc.27366] [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: 10/30/2023] [Revised: 02/16/2024] [Accepted: 03/25/2024] [Indexed: 07/21/2024]
Abstract
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. Here, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets.
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Affiliation(s)
- Johannes Voss
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, California, USA
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3
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Yu F. Origins of the unphysical noncovalent interaction energy curves obtained with the 2011 and 2012 Minnesota density functionals. J Chem Phys 2024; 160:214120. [PMID: 38836783 DOI: 10.1063/5.0212534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 05/22/2024] [Indexed: 06/06/2024] Open
Abstract
With the noncovalent interaction energy curves of the methane dimer [(CH4)2], we have clarified two different origins of the unphysical noncovalent interaction energy curves obtained with the Minnesota density functionals of M11-L, MN12-L, and MN12-SX. For the M11-L functional, the unphysical inflection point on the (CH4)2 interaction energy curve originates from the inclusion of the long-range exchange. As to the MN12-L and MN12-SX functionals, the lack of smoothness restraints results in unphysical inflection points on the corresponding (CH4)2 interaction energy curves. As a result, exchange functionals are as important as dispersion corrections for density functionals to map noncovalent interaction energy surfaces reasonably. Moreover, very highly parameterized functionals with smoothness restraints are suggested for investigating noncovalent interaction energy surfaces.
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Affiliation(s)
- Feng Yu
- Department of Physics, School of Freshmen, Xi'an Technological University, No. 4 Jinhua North Road, Xi'an, Shaanxi 710032, China
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4
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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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5
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Rummel L, Schreiner PR. Advances and Prospects in Understanding London Dispersion Interactions in Molecular Chemistry. Angew Chem Int Ed Engl 2024; 63:e202316364. [PMID: 38051426 DOI: 10.1002/anie.202316364] [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: 10/29/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/07/2023]
Abstract
London dispersion (LD) interactions are the main contribution of the attractive part of the van der Waals potential. Even though LD effects are the driving force for molecular aggregation and recognition, the role of these omnipresent interactions in structure and reactivity had been largely underappreciated over decades. However, in the recent years considerable efforts have been made to thoroughly study LD interactions and their potential as a chemical design element for structures and catalysis. This was made possible through a fruitful interplay of theory and experiment. This review highlights recent results and advances in utilizing LD interactions as a structural motif to understand and utilize intra- and intermolecularly LD-stabilized systems. Additionally, we focus on the quantification of LD interactions and their fundamental role in chemical reactions.
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Affiliation(s)
- Lars Rummel
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392, Giessen, Germany
| | - Peter R Schreiner
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392, Giessen, Germany
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6
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Li H, Mansoori Kermani M, Ottochian A, Crescenzi O, Janesko BG, Truhlar DG, Scalmani G, Frisch MJ, Ciofini I, Adamo C. Modeling Multi-Step Organic Reactions: Can Density Functional Theory Deliver Misleading Chemistry? J Am Chem Soc 2024; 146:6721-6732. [PMID: 38413362 DOI: 10.1021/jacs.3c12713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
Many organic reactions are characterized by a complex mechanism with a variety of transition states and intermediates of different chemical natures. Their correct and accurate theoretical characterization critically depends on the accuracy of the computational method used. In this work, we study a complex ambimodal cycloaddition with five transition states, two intermediates, and three products, and we ask whether density functional theory (DFT) can provide a correct description of this type of complex and multifaceted reaction. Our work fills a gap in that most systematic benchmarks of DFT for chemical reactions have considered much simpler reactions. Our results show that many density functionals not only lead to seriously large errors but also differ from one another in predicting whether the reaction is ambimodal. Only a few of the available functionals provide a balanced description of the complex and multifaceted reactions. The parameters varied in the tested functionals are the ingredients, the treatment of medium-range and nonlocal correlation energy, and the inclusion of Hartree-Fock exchange. These results show a clear need for more benchmarks on the mechanisms of large molecules in complex reactions.
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Affiliation(s)
- Hanwei Li
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, Paris F-75005, France
| | - Maryam Mansoori Kermani
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Alistar Ottochian
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, Paris F-75005, France
| | - Orlando Crescenzi
- Dipartimento di Scienze Chimiche, Università di Napoli Federico II, Complesso Universitario di Monte Sant'Angelo, Via Cinthia, Napoli 80126, Italy
| | - Benjamin G Janesko
- Department of Chemistry & Biochemistry, Texas Christian University, Fort Worth, Texas 76129, United States
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | | | | | - Ilaria Ciofini
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, Paris F-75005, France
| | - Carlo Adamo
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, Paris F-75005, France
- Institut Universitaire de France, 103 Boulevard Saint Michel, Paris F-75005, France
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7
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Wang Z, Han W, Shi R, Han X, Zheng Y, Xu J, Bu XH. Mechanoresponsive Flexible Crystals. JACS AU 2024; 4:279-300. [PMID: 38425899 PMCID: PMC10900217 DOI: 10.1021/jacsau.3c00481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/06/2023] [Accepted: 12/15/2023] [Indexed: 03/02/2024]
Abstract
Flexible crystals have gained significant attention owing to their remarkable pliability, plasticity, and adaptability, making them highly popular in various research and application fields. The main challenges in developing flexible crystals lie in the rational design, preparation, and performance optimization of such crystals. Therefore, a comprehensive understanding of the fundamental origins of crystal flexibility is crucial for establishing evaluation criteria and design principles. This Perspective offers a retrospective analysis of the development of flexible crystals over the past two decades. It summarizes the elastic standards and possible plastic bending mechanisms tailored to diverse flexible crystals and analyzes the assessment of their theoretical basis and applicability. Meanwhile, the compatibility between crystal elasticity and plasticity has been discussed, unveiling the immense prospects of elastic/plastic crystals for applications in biomedicine, flexible electronic devices, and flexible optics. Furthermore, this Perspective presents state-of-the-art experimental avenues and analysis methods for investigating molecular interactions in molecular crystals, which is vital for the future exploration of the mechanisms of crystal flexibility.
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Affiliation(s)
- Zhihua Wang
- School
of Materials Science and Engineering, Smart Sensing Interdisciplinary
Science Center, Frontiers Science Center for New Organic Matter, Nankai University, Tongyan Road 38, Tianjin 300350, P. R. China
| | - Wenqing Han
- School
of Materials Science and Engineering, Smart Sensing Interdisciplinary
Science Center, Frontiers Science Center for New Organic Matter, Nankai University, Tongyan Road 38, Tianjin 300350, P. R. China
| | - Rongchao Shi
- School
of Materials Science and Engineering, Smart Sensing Interdisciplinary
Science Center, Frontiers Science Center for New Organic Matter, Nankai University, Tongyan Road 38, Tianjin 300350, P. R. China
| | - Xiao Han
- School
of Materials Science and Engineering, Smart Sensing Interdisciplinary
Science Center, Frontiers Science Center for New Organic Matter, Nankai University, Tongyan Road 38, Tianjin 300350, P. R. China
| | - Yongshen Zheng
- School
of Materials Science and Engineering, Smart Sensing Interdisciplinary
Science Center, Frontiers Science Center for New Organic Matter, Nankai University, Tongyan Road 38, Tianjin 300350, P. R. China
| | - Jialiang Xu
- School
of Materials Science and Engineering, Smart Sensing Interdisciplinary
Science Center, Frontiers Science Center for New Organic Matter, Nankai University, Tongyan Road 38, Tianjin 300350, P. R. China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300350, P. R. China
| | - Xian-He Bu
- School
of Materials Science and Engineering, Smart Sensing Interdisciplinary
Science Center, Frontiers Science Center for New Organic Matter, Nankai University, Tongyan Road 38, Tianjin 300350, P. R. China
- Collaborative
Innovation Center of Chemical Science and Engineering, Tianjin 300350, P. R. China
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8
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Jijila B, Nirmala V, Selvarengan P, Kavitha D, Arun Muthuraj V, Rajagopal A. Employing neural density functionals to generate potential energy surfaces. J Mol Model 2024; 30:65. [PMID: 38340208 DOI: 10.1007/s00894-024-05834-2] [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: 09/18/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024]
Abstract
CONTEXT With the union of machine learning (ML) and quantum chemistry, amid the debate between machine-learned functionals and human-designed functionals in density functional theory (DFT), this paper aims to demonstrate the generation of potential energy surfaces using computations with machine-learned density functional approximation (ML-DFA). A recent research trend is the application of ML in quantum sciences in the design of density functionals such as DeepMind's Deep Learning model (DeepMind21, DM21). Though science reported the state-of-the-art performance of DM21, the opportunity to utilize DeepMind's pretrained DM21 neural networks in computations in quantum chemistry has not yet been tapped. So far in the literature, the Deep Learning density functionals (DM21) have not been applied to generate potential energy surfaces. While the superior accuracy of DM21 has been reported, there is still a scarcity of publications that apply DM21 in calculations in the field. In this context, for the first time in literature, neural density functionals inferring 2D potential energy surfaces (ML-DFA-PES) based on machine-learned DFA-based computational method is contributed in this paper. This paper reports the ML-DFA-generated PES for C4H8, H2O, H2, and H2+ by employing a pretrained DM21m TensorFlow model with cc-pVDZ basis set. In addition, we also analyze the long-range behavior of DM21 based PES to investigate the ability to describe a system at long ranges. Furthermore, we compare PES diagrams from DM21 with popular DFT functionals (b3lyp/ PW6B95) and CCSD(T). METHODS In this method, 2D potential energy surfaces are obtained using a method that relies upon the neural network's ability to accurately learn the mapping between 3D electron density and exchange-correlation potential. By inserting Deep Learning inference in DFT with a pretrained neural network, self-consistent field (SCF) energy at different geometries along the coordinates of interest is computed, and then, potential energy surfaces are plotted. In this method, first, the electron density is computed mathematically, and this computed 3D electron density is used as a ML feature vector to predict the exchange correlation potential as a ML inference computed by a forward pass of pre-trained DM21 TensorFlow computational graph, followed by the computation of self-consistent field energy at multiple geometries, and then, SCF energies at different bond lengths/angles are plotted as 2D PES. We implement this in a python source code using frameworks such as PySCF and DM21. This paper contributes this implementation in open source. The source code and DM21-DFA-based PES are contributed at https://sites.google.com/view/MLfunctionals-DeepMind-PES .
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Affiliation(s)
- B Jijila
- Queen Mary's College, Chennai, India
| | - V Nirmala
- Queen Mary's College, Chennai, India.
| | - P Selvarengan
- Kalasalingam Academy of Research & Education, Krishnankoil, India
| | - D Kavitha
- Dr. MGR Educational and Research Institute, Chennai, India
| | | | - A Rajagopal
- Indian Institute of Technology, Madras, India
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9
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Brothers EN, Bengali AA, Scalmani G, Janesko BG, Verma P, Truhlar DG, Frisch MJ. Comparing Density Functional Theory Metal-Ligand Bond Dissociation Enthalpies with Experimental Solution-Phase Enthalpies of Activation for Bond Dissociation. J Phys Chem A 2023; 127:9695-9704. [PMID: 37939355 DOI: 10.1021/acs.jpca.3c04838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
The predictive ability of density functional theory is fundamental to its usefulness in chemical applications. Recent work has compared solution-phase enthalpies of activation for metal-ligand bond dissociation to enthalpies of reaction for bond dissociation, and the present work continues those comparisons for 43 density functional methods. The results for ligand dissociation enthalpies of 30 metal-ligand complexes tested in this work reveal significant inadequacies of some functionals as well as challenges from the dispersion corrections to some functionals. The analysis presented here demonstrates the excellent performance of a recent density functional, M11plus, which contains nonlocal rung-3.5 correlation. We also find a good agreement between theory and experiment for some functionals without empirical dispersion corrections such as M06, r2SCAN, M06-L, and revM11, as well as good performance for some functionals with added dispersion corrections such as ωB97X-D (which always has a correction) and BLYP, B3LYP, CAM-B3LYP, and PBE0 when the optional dispersion corrections are added.
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Affiliation(s)
- Edward N Brothers
- Gaussian, Inc., 340 Quinnipiac Street, Wallingford, Connecticut 06492, United States
| | - Ashfaq A Bengali
- Division of Arts and Sciences, Texas A&M University at Qatar, Doha, Qatar
| | - Giovanni Scalmani
- Gaussian, Inc., 340 Quinnipiac Street, Wallingford, Connecticut 06492, United States
| | - Benjamin G Janesko
- Department of Chemistry and Biochemistry, Texas Christian University, Fort Worth, Texas 76110, United States
| | - Pragya Verma
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Michael J Frisch
- Gaussian, Inc., 340 Quinnipiac Street, Wallingford, Connecticut 06492, United States
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10
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Hagg A, Kirschner KN. Open-Source Machine Learning in Computational Chemistry. J Chem Inf Model 2023; 63:4505-4532. [PMID: 37466636 PMCID: PMC10430767 DOI: 10.1021/acs.jcim.3c00643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Indexed: 07/20/2023]
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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Affiliation(s)
- Alexander Hagg
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Electrical Engineering, Mechanical Engineering and Technical Journalism, University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
| | - Karl N. Kirschner
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Computer Science, University of Applied
Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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11
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Kermani MM, Li H, Ottochian A, Crescenzi O, Janesko BG, Scalmani G, Frisch MJ, Ciofini I, Adamo C, Truhlar DG. Barrier Heights for Diels-Alder Transition States Leading to Pentacyclic Adducts: A Benchmark Study of Crowded, Strained Transition States of Large Molecules. J Phys Chem Lett 2023:6522-6531. [PMID: 37449565 DOI: 10.1021/acs.jpclett.3c01309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Theoretical characterization of reactions of complex molecules depends on providing consistent accuracy for the relative energies of intermediates and transition states. Here we employ the DLPNO-CCSD(T) method with core-valence correlation, large basis sets, and extrapolation to the CBS limit to provide benchmark values for Diels-Alder transition states leading to competitive strained pentacyclic adducts. We then used those benchmarks to test a diverse set of wave function and density functional methods for the absolute and relative barrier heights of these transition states. Our results show that only a few of the tested density functionals can predict the absolute barrier heights satisfactorily, although relative barrier heights are more accurate. The most accurate functionals tested are ωB97M-V, M11plus, ωB97X-V, PBE-D3(0), M11, and MN15 with MUDs from best estimates less than 3.0 kcal. These findings can guide selection of density functionals for future studies of crowded, strained transition states of large molecules.
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Affiliation(s)
- Maryam Mansoori Kermani
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Hanwei Li
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, F-75005 Paris, France
| | - Alistar Ottochian
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, F-75005 Paris, France
| | - Orlando Crescenzi
- Dipartimento di Scienze Chimiche, Università di Napoli Federico II, Complesso Universitario di Monte Sant'Angelo, Via Cinthia, 80126 Napoli, Italy
| | - Benjamin G Janesko
- Department of Chemistry & Biochemistry, Texas Christian University, Fort Worth, Texas 76129, United States
| | | | | | - Ilaria Ciofini
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, F-75005 Paris, France
| | - Carlo Adamo
- Chimie ParisTech, PSL Research University, CNRS, Institute of Chemistry for Life and Health Sciences, F-75005 Paris, France
- Institut Universitaire de France, 103 Boulevard Saint Michel, F-75005 Paris, France
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
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