1
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Fu W, Mo Y, Xiao Y, Liu C, Zhou F, Wang Y, Zhou J, Zhang YJ. Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential. J Chem Theory Comput 2024; 20:4533-4544. [PMID: 38828925 DOI: 10.1021/acs.jctc.3c01181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFFs), alongside evaluating the pragmatic utility of these MLFFs. This study introduces SWANI, an optimized neural network potential stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared with that of the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent graph neural network-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI's exceptional capacity for generalization and its proficiency in handling larger molecular systems.
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Affiliation(s)
- Weiqiang Fu
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yujie Mo
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yi Xiao
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Chang Liu
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Feng Zhou
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yang Wang
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Jielong Zhou
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, China
| | - Yingsheng J Zhang
- Beijing StoneWise Technology Co., Ltd., Haidian Street 15, Haidian District, Beijing 100080, 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: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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Wang G, Wang C, Zhang X, Li Z, Zhou J, Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience 2024; 27:109673. [PMID: 38646181 PMCID: PMC11033164 DOI: 10.1016/j.isci.2024.109673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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Affiliation(s)
- Guanjie Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Changrui Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Xuanguang Zhang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zefeng Li
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
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4
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Yan Z, Wei D, Li X, Chung LW. Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning. Nat Commun 2024; 15:4181. [PMID: 38755151 PMCID: PMC11099068 DOI: 10.1038/s41467-024-48453-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024] Open
Abstract
Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e.g., drugs/inhibitors), replacing the expensive QM method. Additionally, two levels of MLPs are combined for the first time to overcome MLP limitations. Our unique MLPs+ONIOM-based QR methods achieve QM-level accuracy with significantly higher efficiency. Furthermore, our refinements provide computational evidence for the existence of bonded and nonbonded forms of the Food and Drug Administration (FDA)-approved drug nirmatrelvir in one SARS-CoV-2 main protease structure. This study highlights that powerful MLPs accelerate QRs for reliable protein-drug complexes, promote broader QR applications and provide more atomistic insights into drug development.
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Affiliation(s)
- Zeyin Yan
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Dacong Wei
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Xin Li
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Lung Wa Chung
- Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China.
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5
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Gould A, Schalk DR, Fleagle ME, Wheeler KA. Benzoyl Valine Quasiracemates: Pairing CF 3 Quasienantiomers with H to t-Butyl. CRYSTAL GROWTH & DESIGN 2024; 24:3967-3976. [PMID: 38708368 PMCID: PMC11066836 DOI: 10.1021/acs.cgd.4c00307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 05/07/2024]
Abstract
Understanding the interplay of structural features responsible for molecular assembly is essential for molecular crystal engineering. When assembling molecules with encoded motifs, first choice supramolecular strategies almost always include robust directional nonbonded contacts. Quasiracemic materials, considered near racemates since cocrystallization occurs with chemically unique components, lack a molecular framework or functional group restrictions, highlighting the importance of molecular shape to molecular assembly. Recently, our group reported quasiracemates derived from benzoyl leucine/phenylalanine derivatives with two points of chemical difference. In this study, we modified the chemical framework with valine and increased the scope of the work by imposing a larger variance in the side chain substituents. Pairing a CF3 component with quasienantiomers that differ iteratively from hydrogen to t-butyl offers an important view into the supramolecular landscape of these materials. Single-crystal X-ray crystallography and lattice energy assessments, coupled with conformational and crystal structure similarity searches, show an elevated degree of isomorphism for many of the targeted 17 racemates and quasiracemates. These benzoyl amino acid molecular architectures create extended hydrogen-bond patterns in the crystal that provide enhanced opportunities to study the shape space and molecular recognition profiles for a diverse family of quasienantiomeric components.
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Affiliation(s)
- Ashah
M. Gould
- Department of Chemistry, Whitworth University, 300 West Hawthorne Road, Spokane, Washington 99251, United States
| | - Danielle R. Schalk
- Department of Chemistry, Whitworth University, 300 West Hawthorne Road, Spokane, Washington 99251, United States
| | - Molly E. Fleagle
- Department of Chemistry, Whitworth University, 300 West Hawthorne Road, Spokane, Washington 99251, United States
| | - Kraig A. Wheeler
- Department of Chemistry, Whitworth University, 300 West Hawthorne Road, Spokane, Washington 99251, United States
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6
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Wan K, He J, Shi X. Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305758. [PMID: 37640376 DOI: 10.1002/adma.202305758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/24/2023] [Indexed: 08/31/2023]
Abstract
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.
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Affiliation(s)
- Kaiwei Wan
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Jianxin He
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
| | - Xinghua Shi
- Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China
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7
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Chen M, Jiang X, Zhang L, Chen X, Wen Y, Gu Z, Li X, Zheng M. The emergence of machine learning force fields in drug design. Med Res Rev 2024; 44:1147-1182. [PMID: 38173298 DOI: 10.1002/med.22008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.
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Affiliation(s)
- Mingan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Xinyu Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Lehan Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoxu Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Yiming Wen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Zhiyong Gu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
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8
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Ge F, Wang R, Qu C, Zheng P, Nandi A, Conte R, Houston PL, Bowman JM, Dral PO. Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine. J Phys Chem Lett 2024; 15:4451-4460. [PMID: 38626460 DOI: 10.1021/acs.jpclett.4c00746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid. Our results show that the errors in the test set do not unambiguously reflect the MLP performance in different simulation tasks, such as relative conformer energies, barriers, vibrational levels, and zero-point vibrational energies. We also offer an easily accessible solution for improving the MLP quality in a simulation-oriented manner, yielding the most precise relative conformer energies and barriers. This solution also passed the stringent test by diffusion Monte Carlo simulations.
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Affiliation(s)
- Fuchun Ge
- 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
| | - Ran Wang
- 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
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Peikun Zheng
- 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
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City L-1511, Luxembourg
| | - Riccardo Conte
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - 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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9
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Shakiba M, Akimov AV. Machine-Learned Kohn-Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics. J Chem Theory Comput 2024; 20:2992-3007. [PMID: 38581699 DOI: 10.1021/acs.jctc.4c00008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2024]
Abstract
In this work, we report a simple, efficient, and scalable machine-learning (ML) approach for mapping non-self-consistent Kohn-Sham Hamiltonians constructed with one kind of density functional to the nearly self-consistent Hamiltonians constructed with another kind of density functional. This approach is designed as a fast surrogate Hamiltonian calculator for use in long nonadiabatic dynamics simulations of large atomistic systems. In this approach, the input and output features are Hamiltonian matrices computed from different levels of theory. We demonstrate that the developed ML-based Hamiltonian mapping method (1) speeds up the calculations by several orders of magnitude, (2) is conceptually simpler than alternative ML approaches, (3) is applicable to different systems and sizes and can be used for mapping Hamiltonians constructed with arbitrary density functionals, (4) requires a modest training data, learns fast, and generates molecular orbitals and their energies with the accuracy nearly matching that of conventional calculations, and (5) when applied to nonadiabatic dynamics simulation of excitation energy relaxation in large systems yields the corresponding time scales within the margin of error of the conventional calculations. Using this approach, we explore the excitation energy relaxation in C60 fullerene and Si75H64 quantum dot structures and derive qualitative and quantitative insights into dynamics in these systems.
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Affiliation(s)
- Mohammad Shakiba
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
| | - Alexey V Akimov
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
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10
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Houston PL, Qu C, Yu Q, Pandey P, Conte R, Nandi A, Bowman JM. No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials. J Chem Theory Comput 2024; 20:3008-3018. [PMID: 38593438 DOI: 10.1021/acs.jctc.4c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Assessments of machine-learning (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutationally invariant polynomial (PIP) method for ethanol, using the widely used (revised) rMD17 data set. We demonstrated that the PIP approach outperformed numerous other methods, e.g., ANI, PhysNet, sGDML, and p-KRR, with respect to precision and notably with respect to speed [Houston et al., J. Chem. Phys. 2022, 156, 044120]. Here, we extend this assessment to the 21-atom aspirin molecule, using the rMD17 data set, with a focus on the speed of evaluation. Both energies and forces are used for training, and the precision of several PIPs is examined for both. Normal mode frequencies, the methyl torsional potential, and 1d vibrational energies for an OH stretch are presented. We show that the PIP approach achieves the level of precision obtained from other ML methods, e.g., atom-centered neural network methods, linear regression ACE, and kernel methods, as reported by Kovács et al. in J. Chem. Theory Comput. 2021, 17, 7696-7711. More significantly, we show that the PIP PESs run much faster than all other ML methods, whose timings were evaluated in that paper. We also show that the PIP PES extrapolates well enough to describe several internal motions of aspirin, including an OH stretch.
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Affiliation(s)
- Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Qi Yu
- Department of Chemistry, Fudan University, Shanghai 200438, P. R. China
| | - Priyanka Pandey
- Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Riccardo Conte
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City L-1511, Luxembourg
| | - Joel M Bowman
- Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
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11
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Pan X, Snyder R, Wang JN, Lander C, Wickizer C, Van R, Chesney A, Xue Y, Mao Y, Mei Y, Pu J, Shao Y. Training machine learning potentials for reactive systems: A Colab tutorial on basic models. J Comput Chem 2024; 45:638-647. [PMID: 38082539 PMCID: PMC10923003 DOI: 10.1002/jcc.27269] [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: 08/31/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 01/18/2024]
Abstract
In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.
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Affiliation(s)
- Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Ryan Snyder
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Jia-Ning Wang
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
| | - Chance Lander
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Carly Wickizer
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Richard Van
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
- Laboratory of Computational Biology, National, Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20824, USA
| | - Andrew Chesney
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Yuanfei Xue
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
| | - Yuezhi Mao
- Department of Chemistry and Biochemistry, San Diego State University, San Diego, CA 92182, USA
| | - Ye Mei
- State Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Jingzhi Pu
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
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12
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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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13
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Chang X, Wu Y, Chu Q, Zhang G, Chen D. Ab Initio Driven Exploration on the Thermal Properties of Al-Li Alloy. ACS APPLIED MATERIALS & INTERFACES 2024. [PMID: 38497105 DOI: 10.1021/acsami.4c01480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Al-Li alloys are feasible and promising additives in advanced energy and propellant systems due to the significantly enhanced heat release and increased specific impulse. The thermal properties of Al-Li alloys directly determine the manufacturing, storage safety, and ignition delay of propellants. In this study, a neural network potential (NNP) is developed to investigate the thermal behaviors of Al-Li alloys from an atomistic perspective. The novel NNP demonstrates an excellent predictive ability for energy, atomic force, mechanical behaviors, phonon vibrations, and dynamic evolutions. A series of NNP-based molecular dynamics simulations are performed to investigate the effect of Li doping on the thermal properties of Al-Li alloys. All calculated results for Al-Li alloys are consistent with experimental values for Al, ensuring their validity in predicting Al-Li interactions. The simulation results suggest that a minor increment in the Li content results in a slight change in the melting point, thermal expansion, and radical distribution functions. These three properties are associated with the lattice characteristics; nonetheless, it causes a substantial reduction in thermal conductivity, which is related to the physical properties of the elements. The lower thermal conductivity allows heat accumulation on the particle surface, thereby speeding up the surface premelt and ignition. This provides an alternative atomic explanation for the improved combustion performance of Al-Li alloys. These findings integrate insights from the field of alloy material science into crucial combustion applications, serving as an atomistic guide for developing manufacturing techniques.
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Affiliation(s)
- Xiaoya Chang
- State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yongchao Wu
- State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Qingzhao Chu
- State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Gang Zhang
- Institute of High Performance Computing Agency for Science Technology and Research (A*STAR), Singapore 138632, Singapore
| | - Dongping Chen
- State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
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14
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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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15
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Montes de Oca-Estévez MJ, Valdés Á, Prosmiti R. A kernel-based machine learning potential and quantum vibrational state analysis of the cationic Ar hydride (Ar 2H +). Phys Chem Chem Phys 2024; 26:7060-7071. [PMID: 38345626 DOI: 10.1039/d3cp05865d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
One of the most fascinating discoveries in recent years, in the cold and low pressure regions of the universe, was the detection of ArH+ and HeH+ species. The identification of such noble gas-containing molecules in space is the key to understanding noble gas chemistry. In the present work, we discuss the possibility of [Ar2H]+ existence as a potentially detectable molecule in the interstellar medium, providing new data on possible astronomical pathways and energetics of this compound. As a first step, a data-driven approach is proposed to construct a full 3D machine-learning potential energy surface (ML-PES) via the reproducing kernel Hilbert space (RKHS) method. The training and testing data sets are generated from CCSD(T)/CBS[56] computations, while a validation protocol is introduced to ensure the quality of the potential. In turn, the resulting ML-PES is employed to compute vibrational levels and molecular spectroscopic constants for the cation. In this way, the most common isotopologue in ISM, [36Ar2H]+, was characterized for the first time, while simultaneously, comparisons with previously reported values available for [40Ar2H]+ are discussed. Our present data could serve as a benchmark for future studies on this system, as well as on higher-order cationic Ar-hydrides of astrophysical interest.
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Affiliation(s)
- María Judit Montes de Oca-Estévez
- Institute of Fundamental Physics (IFF-CSIC), CSIC, Serrano 123, 28006 Madrid, Spain.
- Atelgraphics S.L., Mota de Cuervo 42, 28043, Madrid, Spain
| | - Álvaro Valdés
- Escuela de Física, Universidad Nacional de Colombia, Sede Medellín, A. A., 3840, Medellín, Colombia
| | - Rita Prosmiti
- Institute of Fundamental Physics (IFF-CSIC), CSIC, Serrano 123, 28006 Madrid, Spain.
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16
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Dral PO, Ge F, Hou YF, Zheng P, Chen Y, Barbatti M, Isayev O, Wang C, Xue BX, Pinheiro Jr M, Su Y, Dai Y, Chen Y, Zhang L, Zhang S, Ullah A, Zhang Q, Ou Y. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows. J Chem Theory Comput 2024; 20:1193-1213. [PMID: 38270978 PMCID: PMC10867807 DOI: 10.1021/acs.jctc.3c01203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/29/2023] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.
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Affiliation(s)
- Pavlo O. Dral
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Fuchun Ge
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Yi-Fan Hou
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Peikun Zheng
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Yuxinxin Chen
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Mario Barbatti
- Aix
Marseille University, CNRS, ICR, Marseille 13013, France
- Institut
Universitaire de France, Paris 75231, France
| | - Olexandr Isayev
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Cheng Wang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Bao-Xin Xue
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Max Pinheiro Jr
- Aix
Marseille University, CNRS, ICR, Marseille 13013, France
| | - Yuming Su
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Yiheng Dai
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Yangtao Chen
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Lina Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Shuang Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Arif Ullah
- School
of Physics and Optoelectronic Engineering, Anhui University, Hefei230601, China
| | - Quanhao Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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
| | - Yanchi Ou
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and 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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17
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Bass L, Elder LH, Folescu DE, Forouzesh N, Tolokh IS, Karpatne A, Onufriev AV. Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment. J Chem Theory Comput 2024; 20:396-410. [PMID: 38149593 PMCID: PMC10950260 DOI: 10.1021/acs.jctc.3c00981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.
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Affiliation(s)
- Lewis Bass
- Department of Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Luke H Elder
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Dan E Folescu
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department of Mathematics, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Negin Forouzesh
- Department of Computer Science, California State University, Los Angeles, California 90032, United States
| | - Igor S Tolokh
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Anuj Karpatne
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Alexey V Onufriev
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department of Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24061, United States
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18
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Zhang Y, Xu C, Lan Z. Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations. J Chem Theory Comput 2023. [PMID: 38031422 DOI: 10.1021/acs.jctc.3c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.
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Affiliation(s)
- Yutai Zhang
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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19
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Xia J, Zhang Y, Jiang B. Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing. J Phys Chem A 2023; 127:9874-9883. [PMID: 37943102 DOI: 10.1021/acs.jpca.3c06024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Atomistic neural network potentials have achieved great success in accelerating atomistic simulations in complicated systems in recent years. They are typically based on the atomic decomposition of total properties, truncating the interatomic correlations to a local environment within a given cutoff radius. A more recently developed message passing (MP) neural network framework can, in principle, incorporate nonlocal effects through iteratively correlating some atoms outside the cutoff sphere with atoms inside, a process referred to as MP. However, how the model accuracy depends on the cutoff radius and the MP process has rarely been discussed. In this work, we investigate this dependence using a recursively embedded atom neural network method that possesses both local and MP features, in two representative systems: liquid H2O and solid Al2O3. We focus on how these settings influence predictions for structural and vibrational properties, namely, radial distribution functions (RDFs) and vibrational density of states (VDOSs). We find that while MP lowers test errors of energy and forces in general, it may not improve the prediction for RDFs and/or VDOSs if direct interatomic correlations in the local environment are insufficiently described. A cutoff radius exceeding the first neighbor shell is necessary, beyond which involving MP quickly enhances the model accuracy until convergence. This is a potentially more efficient way to increase the model accuracy than directly increasing the cutoff radius, especially with more memory savings in the GPU implementation. Our findings also suggest that using the mean test error as the measure of the model accuracy alone is inadequate.
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Affiliation(s)
- Junfan Xia
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yaolong Zhang
- École Polytechnique FFlytech de Lausanne, 1015 Lausanne, Switzerland
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
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20
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Zhang L, Hou YF, Ge F, Dral PO. Energy-conserving molecular dynamics is not energy conserving. Phys Chem Chem Phys 2023; 25:23467-23476. [PMID: 37614218 DOI: 10.1039/d3cp03515h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Molecular dynamics (MD) is a widely-used tool for simulating molecular and materials properties. It is common wisdom that molecular dynamics simulations should obey physical laws and, hence, lots of effort is put into ensuring that molecular dynamics simulations are energy conserving. The emergence of machine learning (ML) potentials for MD leads to a growing realization that monitoring conservation of energy during simulations is of low utility because the dynamics is often unphysically dissociative. Other ML methods for MD are not based on a potential and provide only forces or trajectories which are reasonable but not necessarily energy-conserving. Here we propose to clearly distinguish between the simulation-energy and true-energy conservation and highlight that the simulations should focus on decreasing the degree of true-energy non-conservation. We introduce very simple, new criteria for evaluating the quality of molecular dynamics by estimating the degree of true-energy non-conservation and we demonstrate their practical utility on an example of infrared spectra simulations. These criteria are more important and intuitive than simply evaluating the quality of the ML potential energies and forces as is commonly done and can be applied universally, e.g., even for trajectories with unknown or discontinuous potential energy. Such an approach introduces new standards for evaluating MD by focusing on the true-energy conservation and can help in developing more accurate methods for simulating molecular and materials properties.
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Affiliation(s)
- Lina Zhang
- 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.
| | - Yi-Fan Hou
- 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.
| | - Fuchun Ge
- 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.
| | - 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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21
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Zeng J, Zhang D, Lu D, Mo P, Li Z, Chen Y, Rynik M, Huang L, Li Z, Shi S, Wang Y, Ye H, Tuo P, Yang J, Ding Y, Li Y, Tisi D, Zeng Q, Bao H, Xia Y, Huang J, Muraoka K, Wang Y, Chang J, Yuan F, Bore SL, Cai C, Lin Y, Wang B, Xu J, Zhu JX, Luo C, Zhang Y, Goodall REA, Liang W, Singh AK, Yao S, Zhang J, Wentzcovitch R, Han J, Liu J, Jia W, York DM, E W, Car R, Zhang L, Wang H. DeePMD-kit v2: A software package for deep potential models. J Chem Phys 2023; 159:054801. [PMID: 37526163 PMCID: PMC10445636 DOI: 10.1063/5.0155600] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.
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Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Denghui Lu
- HEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, People’s Republic of China
| | - Pinghui Mo
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | - Zeyu Li
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Yixiao Chen
- Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08540, USA
| | - Marián Rynik
- Department of Experimental Physics, Comenius University, Mlynská Dolina F2, 842 48 Bratislava, Slovakia
| | - Li’ang Huang
- Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, People’s Republic of China
| | | | - Shaochen Shi
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Haotian Ye
- Yuanpei College, Peking University, Beijing 100871, People’s Republic of China
| | - Ping Tuo
- AI for Science Institute, Beijing 100080, People’s Republic of China
| | - Jiabin Yang
- Baidu, Inc., Beijing, People’s Republic of China
| | | | - Yifan Li
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Qiyu Zeng
- Department of Physics, National University of Defense Technology, Changsha, Hunan 410073, People’s Republic of China
| | | | - Yu Xia
- ByteDance Research, Zhonghang Plaza, No. 43, North 3rd Ring West Road, Haidian District, Beijing, People’s Republic of China
| | | | - Koki Muraoka
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yibo Wang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Fengbo Yuan
- DP Technology, Beijing 100080, People’s Republic of China
| | - Sigbjørn Løland Bore
- Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, 0315 Oslo, Norway
| | | | - Yinnian Lin
- Wangxuan Institute of Computer Technology, Peking University, Beijing 100871, People’s Republic of China
| | - Bo Wang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, People’s Republic of China
| | - Jiayan Xu
- School of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast BT9 5AG, United Kingdom
| | - Jia-Xin Zhu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People’s Republic of China
| | - Chenxing Luo
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, USA
| | - Yuzhi Zhang
- DP Technology, Beijing 100080, People’s Republic of China
| | | | - Wenshuo Liang
- DP Technology, Beijing 100080, People’s Republic of China
| | - Anurag Kumar Singh
- Department of Data Science, Indian Institute of Technology, Palakkad, Kerala, India
| | - Sikai Yao
- DP Technology, Beijing 100080, People’s Republic of China
| | - Jingchao Zhang
- NVIDIA AI Technology Center (NVAITC), Santa Clara, California 95051, USA
| | | | - Jiequn Han
- Center for Computational Mathematics, Flatiron Institute, New York, New York 10010, USA
| | - Jie Liu
- College of Electrical and Information Engineering, Hunan University, Changsha, People’s Republic of China
| | | | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA
| | | | - Roberto Car
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
| | | | - Han Wang
- Author to whom correspondence should be addressed:
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22
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Mortazavi B, Zhuang X, Rabczuk T, Shapeev AV. Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials. MATERIALS HORIZONS 2023; 10:1956-1968. [PMID: 37014053 DOI: 10.1039/d3mh00125c] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a growing interest has been developed in the replacement of empirical interatomic potentials (EIPs) with MLIPs, in order to conduct more accurate and reliable molecular dynamics calculations. As an exciting novel progress, in the last couple of years the applications of MLIPs have been extended towards the analysis of mechanical and failure responses, providing novel opportunities not heretofore efficiently achievable, neither by EIPs nor by density functional theory (DFT) calculations. In this minireview, we first briefly discuss the basic concepts of MLIPs and outline popular strategies for developing a MLIP. Next, by considering several examples of recent studies, the robustness of MLIPs in the analysis of the mechanical properties will be highlighted, and their advantages over EIP and DFT methods will be emphasized. MLIPs furthermore offer astonishing capabilities to combine the robustness of the DFT method with continuum mechanics, enabling the first-principles multiscale modeling of mechanical properties of nanostructures at the continuum level. Last but not least, the common challenges of MLIP-based molecular dynamics simulations of mechanical properties are outlined and suggestions for future investigations are proposed.
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Affiliation(s)
- Bohayra Mortazavi
- Chair of Computational Science and Simulation Technology, Department of Mathematics and Physics, Leibniz Universität Hannover, Appelstraße 11, 30167 Hannover, Germany.
- Cluster of Excellence PhoenixD (Photonics, Optics, And Engineering-Innovation Across Disciplines), Gottfried Wilhelm Leibniz Universität Hannover, Hannover, Germany
| | - Xiaoying Zhuang
- Chair of Computational Science and Simulation Technology, Department of Mathematics and Physics, Leibniz Universität Hannover, Appelstraße 11, 30167 Hannover, Germany.
- College of Civil Engineering, Department of Geotechnical Engineering, Tongji University, 1239 Siping Road, Shanghai, China
| | - Timon Rabczuk
- Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, 99423 Weimar, Germany
| | - Alexander V Shapeev
- Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Bolshoy Bulvar 30, Moscow, 143026, Russia.
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23
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Staub R, Gantzer P, Harabuchi Y, Maeda S, Varnek A. Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson's Catalyst Case. Molecules 2023; 28:molecules28114477. [PMID: 37298952 DOI: 10.3390/molecules28114477] [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: 04/02/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Ab initio kinetic studies are important to understand and design novel chemical reactions. While the Artificial Force Induced Reaction (AFIR) method provides a convenient and efficient framework for kinetic studies, accurate explorations of reaction path networks incur high computational costs. In this article, we are investigating the applicability of Neural Network Potentials (NNP) to accelerate such studies. For this purpose, we are reporting a novel theoretical study of ethylene hydrogenation with a transition metal complex inspired by Wilkinson's catalyst, using the AFIR method. The resulting reaction path network was analyzed by the Generative Topographic Mapping method. The network's geometries were then used to train a state-of-the-art NNP model, to replace expensive ab initio calculations with fast NNP predictions during the search. This procedure was applied to run the first NNP-powered reaction path network exploration using the AFIR method. We discovered that such explorations are particularly challenging for general purpose NNP models, and we identified the underlying limitations. In addition, we are proposing to overcome these challenges by complementing NNP models with fast semiempirical predictions. The proposed solution offers a generally applicable framework, laying the foundations to further accelerate ab initio kinetic studies with Machine Learning Force Fields, and ultimately explore larger systems that are currently inaccessible.
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Affiliation(s)
- Ruben Staub
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo 001-0021, Japan
| | - Philippe Gantzer
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo 001-0021, Japan
| | - Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo 001-0021, Japan
- Japan Science and Technology Agency (JST), ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo 060-0810, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo 060-0810, Japan
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo 001-0021, Japan
- Japan Science and Technology Agency (JST), ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo 060-0810, Japan
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo 060-0810, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba 305-0044, Japan
| | - Alexandre Varnek
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo 001-0021, Japan
- Laboratory of Chemoinformatics, UMR 7140, CNRS, University of Strasbourg, 67081 Strasbourg, France
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24
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Blücher S, Müller KR, Chmiela S. Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence. J Chem Theory Comput 2023. [PMID: 37156733 PMCID: PMC10373489 DOI: 10.1021/acs.jctc.2c01304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many equivariances and invariances due to physical symmetries can be incorporated into the kernel function to compensate for much larger data sets. So far, the scalability of kernel machines has however been hindered by its quadratic memory and cubical runtime complexity in the number of training points. While it is known that iterative Krylov subspace solvers can overcome these burdens, their convergence crucially relies on effective preconditioners, which are elusive in practice. Effective preconditioners need to partially presolve the learning problem in a computationally cheap and numerically robust manner. Here, we consider the broad class of Nyström-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods aim to identify a representative subset of inducing (kernel) columns to approximate the dominant kernel spectrum.
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Affiliation(s)
- Stefan Blücher
- BIFOLD-Berlin Institue for the Foundations of Learning and Data, 10587 Berlin, Germany
- Technische Universität Berlin, Machine Learning Group, 10587 Berlin, Germany
| | - Klaus-Robert Müller
- BIFOLD-Berlin Institue for the Foundations of Learning and Data, 10587 Berlin, Germany
- Technische Universität Berlin, Machine Learning Group, 10587 Berlin, Germany
- Department of Artificial Intelligence, Korea University, Seoul 136-713, Korea
- Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
- Google Research, Brain Team, 10117 Berlin, Germany
| | - Stefan Chmiela
- BIFOLD-Berlin Institue for the Foundations of Learning and Data, 10587 Berlin, Germany
- Technische Universität Berlin, Machine Learning Group, 10587 Berlin, Germany
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25
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Morado J, Mortenson PN, Nissink JWM, Essex JW, Skylaris CK. Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins. J Chem Inf Model 2023; 63:2810-2827. [PMID: 37071825 PMCID: PMC10170518 DOI: 10.1021/acs.jcim.2c01510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials.
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Affiliation(s)
- João Morado
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Paul N Mortenson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - J Willem M Nissink
- Computational Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Chris-Kriton Skylaris
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
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26
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Ricci E, Vergadou N. Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers. J Phys Chem B 2023; 127:2302-2322. [PMID: 36888553 DOI: 10.1021/acs.jpcb.2c06354] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.
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Affiliation(s)
- Eleonora Ricci
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
- Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
| | - Niki Vergadou
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
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27
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Sprueill HW, Bilbrey JA, Pang Q, Sushko PV. Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu-Ni multilayers. J Chem Phys 2023; 158:114103. [PMID: 36948793 DOI: 10.1063/5.0133023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023] Open
Abstract
Neural network potentials (NNPs) can greatly accelerate atomistic simulations relative to ab initio methods, allowing one to sample a broader range of structural outcomes and transformation pathways. In this work, we demonstrate an active sampling algorithm that trains an NNP that is able to produce microstructural evolutions with accuracy comparable to those obtained by density functional theory, exemplified during structure optimizations for a model Cu-Ni multilayer system. We then use the NNP, in conjunction with a perturbation scheme, to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP. The code to implement our active learning strategy and NNP-driven stochastic shear simulations is openly available at https://github.com/pnnl/Active-Sampling-for-Atomistic-Potentials.
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Affiliation(s)
- Henry W Sprueill
- National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - Jenna A Bilbrey
- National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - Qin Pang
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, USA
| | - Peter V Sushko
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99352, USA
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28
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Sit MK, Das S, Samanta K. Semiclassical Dynamics on Machine-Learned Coupled Multireference Potential Energy Surfaces: Application to the Photodissociation of the Simplest Criegee Intermediate. J Phys Chem A 2023; 127:2376-2387. [PMID: 36856588 DOI: 10.1021/acs.jpca.2c07229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Determination of high-dimensional potential energy surfaces (PESs) and nonadiabatic couplings have always been quite challenging. To this end, machine learning (ML) models, trained with a finite set of ab initio data, allow accurate prediction of such properties. To express the PESs in terms of atomic contributions is the cornerstone of any ML based technique because it can be easily scaled to large systems. In this work, we have constructed high fidelity PESs and nonadiabatic coupling terms at the CASSCF level of ab initio data using a machine learning technique, namely, kernel-ridge regression. Additional MRCI-level calculations were carried out to assess the quality of the PESs. We use these machine-learned PESs and nonadiabatic couplings to simulate excited-state molecular dynamics based on Tully's fewest-switches surface hopping method (FSSH). FSSH is a semiclassical method in which nuclei move on the PESs due to the electrons according to the laws of classical mechanics. Nonadiabatic effects are taken into account in terms of transitions between PESs. We apply this scheme to study the O-O photodissociation of the simplest Criegee intermediate (CH2OO). The FSSH trajectories were initiated on the lowest optically bright singlet excited state (S2) and propagated along the three most important internal coordinates, namely, O-O and C-O bond distances and the COO bond angle. Some of the trajectories end up on energetically lower PESs as a result of radiationless transfer through conical intersections. All of the trajectories lead to the dissociation of the O-O bond due to the dissociative nature of the excited PESs through one of the two dissociative channels. The simulation reveals that there is about 88.4% probability of dissociation through the lower channel leading to the H2CO (X1A1) and O (1D) products, whereas there is only 11.6% probability of dissociation through the upper channel leading to H2CO (a3A″) and O (3P) products.
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Affiliation(s)
- Mahesh K Sit
- School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Argul, Odisha 752050, India
| | - Subhasish Das
- School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Argul, Odisha 752050, India
| | - Kousik Samanta
- School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Argul, Odisha 752050, India
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29
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Schmitz G, Schnieder B. Adaptive regularized Gaussian process regression for application in the context of hydrogen adsorption on graphene sheets. J Comput Chem 2023; 44:732-744. [PMID: 36382688 DOI: 10.1002/jcc.27035] [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: 07/08/2022] [Revised: 09/21/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022]
Abstract
We present a Gaussian process regression (GPR) scheme with an adaptive regularization scheme applied to the QM7 and QM9 test set, several protonated water clusters and specifically to the problem of atomic hydrogen adsorption on graphene sheets. For the last system our goal is to achieve good predictive accuracy with only a few training points. Therefore, we assess for these systems a self-correcting multilayer GPR model, in which the prediction is corrected by a chain of additional GPR models. In our adaptive regularization scheme, we impose no noise on the training data, but use an approach based on the data itself to account for its impurity. The strength of this strategy is that the data points are treated differently based on their importance and that the regularization can still be controlled by a single parameter. We assess how the accuracy of the prediction depends on this parameter. We can show that the new regularization scheme as well as the multilayer approach results in more robust predictors. Furthermore, we demonstrate that the predictor can be in good agreement with the density-functional theory results.
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Affiliation(s)
- Gunnar Schmitz
- Theoretische Chemie, Ruhr-Universität Bochum, Bochum, Germany
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30
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Zeng J, Tao Y, Giese TJ, York DM. QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery. J Chem Theory Comput 2023; 19:1261-1275. [PMID: 36696673 PMCID: PMC9992268 DOI: 10.1021/acs.jctc.2c01172] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
We report QDπ-v1.0 for modeling the internal energy of drug molecules containing H, C, N, and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning potential correction (QM/Δ-MLP) that uses a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model that is corrected to a quantitatively high-level of accuracy through a deep-learning potential (DeepPot-SE). The model has the advantage that it is able to properly treat electrostatic interactions and handle changes in charge/protonation states. The model is trained against reference data computed at the ωB97X/6-31G* level (as in the ANI-1x data set) and compared to several other approximate semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3, MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QDπ model is demonstrated to be accurate for a wide range of intra- and intermolecular interactions (despite its intended use as an internal energy model) and has shown to perform exceptionally well for relative protonation/deprotonation energies and tautomers. An example application to model reactions involved in RNA strand cleavage catalyzed by protein and nucleic acid enzymes illustrates QDπ has average errors less than 0.5 kcal/mol, whereas the other models compared have errors over an order of magnitude greater. Taken together, this makes QDπ highly attractive as a potential force field model for drug discovery.
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Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Yujun Tao
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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31
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Zhai Y, Caruso A, Bore SL, Luo Z, Paesani F. A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions? J Chem Phys 2023; 158:084111. [PMID: 36859071 DOI: 10.1063/5.0142843] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials. In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven, many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram. We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water, but provides a less accurate description of the vapor-liquid equilibrium properties. This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions. An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties, but losing accuracy in the description of the liquid properties. These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict the properties for state points that are not explicitly included in the training process. The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.
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Affiliation(s)
- Yaoguang Zhai
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Alessandro Caruso
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Sigbjørn Løland Bore
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Zhishang Luo
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
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32
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Pinheiro M, Zhang S, Dral PO, Barbatti M. WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets. Sci Data 2023; 10:95. [PMID: 36792601 PMCID: PMC9931705 DOI: 10.1038/s41597-023-01998-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 02/01/2023] [Indexed: 02/17/2023] Open
Abstract
Multidimensional surfaces of quantum chemical properties, such as potential energies and dipole moments, are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular configurational spaces. Here we composed the WS22 database covering several quantum mechanical (QM) properties (including potential energies, forces, dipole moments, polarizabilities, HOMO, and LUMO energies) for ten flexible organic molecules of increasing complexity and with up to 22 atoms. This database consists of 1.18 million equilibrium and non-equilibrium geometries carefully sampled from Wigner distributions centered at different equilibrium conformations (either at the ground or excited electronic states) and further augmented with interpolated structures. The diversity of our datasets is demonstrated by visualizing the geometries distribution with dimensionality reduction as well as via comparison of statistical features of the QM properties with those available in existing datasets. Our sampling targets broader quantum mechanical distribution of the configurational space than provided by commonly used sampling through classical molecular dynamics, upping the challenge for machine learning models.
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Affiliation(s)
- Max Pinheiro
- Aix Marseille University, CNRS, ICR, Marseille, France.
| | - Shuang Zhang
- 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, China
| | - 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, China
| | - Mario Barbatti
- Aix Marseille University, CNRS, ICR, Marseille, France. .,Institut Universitaire de France, 75231, Paris, France.
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33
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Zaverkin V, Holzmüller D, Bonfirraro L, Kästner J. Transfer learning for chemically accurate interatomic neural network potentials. Phys Chem Chem Phys 2023; 25:5383-5396. [PMID: 36748821 DOI: 10.1039/d2cp05793j] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Developing machine learning-based interatomic potentials from ab initio electronic structure methods remains a challenging task for computational chemistry and materials science. This work studies the capability of transfer learning, in particular discriminative fine-tuning, for efficiently generating chemically accurate interatomic neural network potentials on organic molecules from the MD17 and ANI data sets. We show that pre-training the network parameters on data obtained from density functional calculations considerably improves the sample efficiency of models trained on more accurate ab initio data. Additionally, we show that fine-tuning with energy labels alone can suffice to obtain accurate atomic forces and run large-scale atomistic simulations, provided a well-designed fine-tuning data set. We also investigate possible limitations of transfer learning, especially regarding the design and size of the pre-training and fine-tuning data sets. Finally, we provide GM-NN potentials pre-trained and fine-tuned on the ANI-1x and ANI-1ccx data sets, which can easily be fine-tuned on and applied to organic molecules.
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Affiliation(s)
- Viktor Zaverkin
- Faculty of Chemistry, Institute for Theoretical Chemistry, University of Stuttgart, Germany.
| | - David Holzmüller
- Faculty of Mathematics and Physics, Institute for Stochastics and Applications, University of Stuttgart, Germany.
| | - Luca Bonfirraro
- Faculty of Chemistry, Institute for Theoretical Chemistry, University of Stuttgart, Germany.
| | - Johannes Kästner
- Faculty of Chemistry, Institute for Theoretical Chemistry, University of Stuttgart, Germany.
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34
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Kee CW. Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities. Molecules 2023; 28:molecules28041715. [PMID: 36838703 PMCID: PMC9966076 DOI: 10.3390/molecules28041715] [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: 12/31/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/25/2023] Open
Abstract
Through the lens of organocatalysis and phase transfer catalysis, we will examine the key components to calculate or predict catalysis-performance metrics, such as turnover frequency and measurement of stereoselectivity, via computational chemistry. The state-of-the-art tools available to calculate potential energy and, consequently, free energy, together with their caveats, will be discussed via examples from the literature. Through various examples from organocatalysis and phase transfer catalysis, we will highlight the challenges related to the mechanism, transition state theory, and solvation involved in translating calculated barriers to the turnover frequency or a metric of stereoselectivity. Examples in the literature that validated their theoretical models will be showcased. Lastly, the relevance and opportunity afforded by machine learning will be discussed.
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Affiliation(s)
- Choon Wee Kee
- Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Agency for Science, Technology and Research (A*STAR), 1 Pesek Road, Jurong Island, Singapore 627833, Republic of Singapore
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35
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Sours T, Kulkarni AR. Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2023; 127:1455-1463. [PMID: 36733763 PMCID: PMC9885523 DOI: 10.1021/acs.jpcc.2c08429] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 12/20/2022] [Indexed: 06/18/2023]
Abstract
Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) data set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivate further MLP development for nanoporous materials with near-ab initio accuracy.
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36
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Bowman JM, Qu C, Conte R, Nandi A, Houston PL, Yu Q. Δ-Machine Learned Potential Energy Surfaces and Force Fields. J Chem Theory Comput 2023; 19:1-17. [PMID: 36527383 DOI: 10.1021/acs.jctc.2c01034] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
There has been great progress in developing machine-learned potential energy surfaces (PESs) for molecules and clusters with more than 10 atoms. Unfortunately, this number of atoms generally limits the level of electronic structure theory to less than the "gold standard" CCSD(T) level. Indeed, for the well-known MD17 dataset for molecules with 9-20 atoms, all of the energies and forces were obtained with DFT calculations (PBE). This Perspective is focused on a Δ-machine learning method that we recently proposed and applied to bring DFT-based PESs to close to CCSD(T) accuracy. This is demonstrated for hydronium, N-methylacetamide, acetyl acetone, and ethanol. For 15-atom tropolone, it appears that special approaches (e.g., molecular tailoring, local CCSD(T)) are needed to obtain the CCSD(T) energies. A new aspect of this approach is the extension of Δ-machine learning to force fields. The approach is based on many-body corrections to polarizable force field potentials. This is examined in detail using the TTM2.1 water potential. The corrections make use of our recent CCSD(T) datasets for 2-b, 3-b, and 4-b interactions for water. These datasets were used to develop a new fully ab initio potential for water, termed q-AQUA.
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Affiliation(s)
- Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Chen Qu
- Independent Researcher, Toronto, Canada 66777
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.,Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
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37
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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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38
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Tang D, Jia L, Shen L, Fang WH. Fewest-Switches Surface Hopping with Long Short-Term Memory Networks. J Phys Chem Lett 2022; 13:10377-10387. [PMID: 36317657 DOI: 10.1021/acs.jpclett.2c02299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The mixed quantum-classical dynamical simulation is essential for studying nonadiabatic phenomena in photophysics and photochemistry. In recent years, many machine learning models have been developed to accelerate the time evolution of the nuclear subsystem. Herein, we implement long short-term memory (LSTM) networks as a propagator to accelerate the time evolution of the electronic subsystem during the fewest-switches surface hopping (FSSH) simulations. A small number of reference trajectories are generated using the original FSSH method, and then the LSTM networks can be built, accompanied by careful examination of typical LSTM-FSSH trajectories that employ the same initial condition and random numbers as the corresponding reference. The constructed network is applied to FSSH to further produce a trajectory ensemble to reveal the mechanism of nonadiabatic processes. Taking Tully's three models as test systems, we qualitatively reproduced the collective results. This work demonstrates that LSTM can be applied to the most popular surface hopping simulations.
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Affiliation(s)
- Diandong Tang
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Luyang Jia
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Lin Shen
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
- Yantai-Jingshi Institute of Material Genome Engineering, Yantai 265505, Shandong, China
| | - Wei-Hai Fang
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
- Yantai-Jingshi Institute of Material Genome Engineering, Yantai 265505, Shandong, China
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39
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Snyder R, Kim B, Pan X, Shao Y, Pu J. Facilitating ab initio QM/MM free energy simulations by Gaussian process regression with derivative observations. Phys Chem Chem Phys 2022; 24:25134-25143. [PMID: 36222412 PMCID: PMC11095978 DOI: 10.1039/d2cp02820d] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results.
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Affiliation(s)
- Ryan Snyder
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N. Blackford St., Indianapolis, IN 46202, USA.
| | - Bryant Kim
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N. Blackford St., Indianapolis, IN 46202, USA.
| | - Xiaoliang Pan
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, USA.
| | - Yihan Shao
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, USA.
| | - Jingzhi Pu
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N. Blackford St., Indianapolis, IN 46202, USA.
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40
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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 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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [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
| | - Max Pinheiro
- Aix Marseille University, CNRS, ICR, 13013Marseille, France
| | - 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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41
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Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2022-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.
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Affiliation(s)
- David Kuntz
- Department of Chemistry , University of North Texas , Denton , TX 76201 , USA
| | - Angela K. Wilson
- Department of Chemistry , Michigan State University , East Lansing , MI 48824 , USA
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42
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Young TA, Johnston-Wood T, Zhang H, Duarte F. Reaction dynamics of Diels-Alder reactions from machine learned potentials. Phys Chem Chem Phys 2022; 24:20820-20827. [PMID: 36004770 DOI: 10.1039/d2cp02978b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Recent advances in the development of reactive machine-learned potentials (MLPs) promise to transform reaction modelling. However, such methods have remained computationally expensive and limited to experts. Here, we employ different MLP methods (ACE, NequIP, GAP), combined with automated fitting and active learning, to study the reaction dynamics of representative Diels-Alder reactions. We demonstrate that the ACE and NequIP MLPs can consistently achieve chemical accuracy (±1 kcal mol-1) to the ground-truth surface with only a few hundred reference calculations. These strategies are shown to enable routine ab initio-quality classical and quantum dynamics, and obtain dynamical quantities such as product ratios and free energies from non-static methods. For ambimodal reactions, product distributions were found to be strongly dependent on the QM method and less so on the type of dynamics propagated.
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Affiliation(s)
- Tom A Young
- Chemistry Research Laboratory, 12 Mansfield Road, Oxford, OX1 3TA, UK.
| | | | - Hanwen Zhang
- Chemistry Research Laboratory, 12 Mansfield Road, Oxford, OX1 3TA, UK.
| | - Fernanda Duarte
- Chemistry Research Laboratory, 12 Mansfield Road, Oxford, OX1 3TA, UK.
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43
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Li J, Lopez SA. A Look Inside the Black Box of Machine Learning Photodynamics Simulations. Acc Chem Res 2022; 55:1972-1984. [PMID: 35796602 DOI: 10.1021/acs.accounts.2c00288] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
ConspectusPhotochemical reactions are of great importance in chemistry, biology, and materials science because they take advantage of a renewable energy source, mild reaction conditions, and high atom economy. Light absorption can excite molecules to a higher energy electronic state of the same spin multiplicity. The following nonadiabatic processes induce molecular transformations that afford exotic molecular architectures and high-energy-isomers that are inaccessible by thermal means. Computational simulations now complement time-resolved instrumentation to reveal ultrafast excited-state mechanistic information for photochemical reactions that is essential in disentangling elusive spectroscopic features, excited-state lifetimes, and excited-state mechanistic critical points. Nonadiabatic molecular dynamics (NAMD), powered by surface hopping techniques, is among the most widely applied techniques to model the photochemical reactions of medium-sized molecules. However, the computational efficiency is limited because of the requisite thousands of multiconfigurational quantum-chemical calculations multiplied by hundreds of trajectories. Machine learning (ML) has emerged as a revolutionary force in computational chemistry to predict the outcome of the resource-intensive multiconfigurational calculations on the fly. An ML potential trained with a substantial set of quantum-chemical calculations can predict the energies and forces with errors under chemical accuracy at a negligible cost. The integration of ML potentials in NAMD dramatically extends the maximum simulation time scale by ∼10 000-fold to the nanosecond regime.In this Account, we present a comprehensive demonstration of ML photodynamics simulations and summarize our most recent applications in resolving complex photochemical reactions. First, we address three fundamental components of ML techniques for photodynamics simulations: the quantum-chemical data set, the ML potential, and NAMD. Second, we describe best practices in building training data and our procedure toward training the ML photodynamics model with our recent literature contributions. We introduce a convenient training data generation scheme combining Wigner sampling and geometrical interpolation. It trains reliable and effective ML potentials suitable for subsequent active learning to detect undersampled data. We demonstrate how active learning automatically discovers new mechanistic pathways and reproduces experimental results. We point out that atomic permutation is an essential data augmentation approach to improve the learnability of distance-based molecular descriptors for highly symmetric molecules. Third, we demonstrate the utility of ML-photodynamics by showing the results of ML photodynamics simulations of (1) photo-torquoselective 4π disrotatory electrocyclic ring closing of norbornyl cyclohexadiene, which reveals a thermal conversion from experimentally unobserved intermediates to the reactant in 1 ns; (2) [2 + 2] photocycloaddition of substituted [3]-syn-ladderdienes in competition with 4π and 6π electrocyclic ring-opening reactions, uncovering substituent effects to explain the reported increased quantum yield of substituted cubane precursors; and (3) photochemical 4π disrotatory electrocyclic reactions of fluorobenzenes in nanoseconds with XMS-CASPT2-level training data. We expect this Account to broaden understanding of ML photodynamics and inspire future developments and applications to increasingly large molecules within complex environments on long time scales.
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Affiliation(s)
- Jingbai Li
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Steven A Lopez
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
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44
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Meng Q, Chen J, Ma J, Zhang X, Chen J. Adiabatic models for the quantum dynamics of surface scattering with lattice effects. Phys Chem Chem Phys 2022; 24:16415-16436. [PMID: 35766107 DOI: 10.1039/d2cp01560a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this contribution, we review models for the lattice effects in quantum dynamics calculations on surface scattering, which is important to modeling heterogeneous catalysis for achieving an interpretation of experimental measurements. Unlike dynamics models for reactions in the gas phase, those for heterogeneous reactions have to include the effects of the surface. For manageable computational costs in calculations, the effects of static surface (SS) are firstly modeled as this is simply and easily implemented. Then, the SS model has to be improved to include the effects of the flexible surface, that is the lattice effects. To do this, various surface models have been designed where the coordinates of the surface atoms are introduced in the Hamiltonian operator, especially those of the top surface atom. Based on this model Hamiltonian operator, extensive multi-dimension quantum dynamics calculations can be performed to recover the lattice effects. Here, we first review an overview of the techniques in constructing the Hamiltonian operator, which is a sum of the kinetic energy operator (KEO) and potential energy surface (PES). Since the PES containing the coordinates of the surface atoms in a cell is still expensive, the SS model is often accepted. We consider a mathematical model, called the coupled harmonic oscillator (CHO) model, to introduce the concepts of adiabatic and diabatic representations for separating the molecule and surface. Under the adiabatic model, we further introduce the expansion model where the potential function is Taylor expanded around the optimized geometry of the surface. By an expansion model truncated at the first and second order, various coupling surface models between the molecule and surface are derived. Moreover, by further and deeply understanding the adiabatic representation, an effective Hamiltonian operator is obtained by optimizing the total wave function in factorized form. By this factorized form of wave function and effective Hamiltonian operator, the geometry phase of the surface wave function is theoretically found. This theoretical prediction may be measured by carefully designing experiments. Finally, discussions on the adiabatic representation, the PES construction, and possibility of the classical-dynamics solutions are given. Based on these discussions, a simple outlook on the dynamics of photocatalytics is finally given.
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Affiliation(s)
- Qingyong Meng
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Junbo Chen
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China. .,Xi'an Modern Chemistry Research Institute, China North Industries Group Corp., Ltd., East Zhangba Road 168, 710065 Xi'an, China
| | - Jianxing Ma
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Xingyu Zhang
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Jun Chen
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Yangqiao Road West 155, 350002 Fuzhou, China.,Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Optoelectronic Industry Base at High-tech Zone, 350108 Fuzhou, China
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45
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Bowman JM, Qu C, Conte R, Nandi A, Houston PL, Yu Q. The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials. J Chem Phys 2022; 156:240901. [DOI: 10.1063/5.0089200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
There has been great progress in developing methods for machine-learned potential energy surfaces. There have also been important assessments of these methods by comparing so-called learning curves on datasets of electronic energies and forces, notably the MD17 database. The dataset for each molecule in this database generally consists of tens of thousands of energies and forces obtained from DFT direct dynamics at 500 K. We contrast the datasets from this database for three “small” molecules, ethanol, malonaldehyde, and glycine, with datasets we have generated with specific targets for the potential energy surfaces (PESs) in mind: a rigorous calculation of the zero-point energy and wavefunction, the tunneling splitting in malonaldehyde, and, in the case of glycine, a description of all eight low-lying conformers. We found that the MD17 datasets are too limited for these targets. We also examine recent datasets for several PESs that describe small-molecule but complex chemical reactions. Finally, we introduce a new database, “QM-22,” which contains datasets of molecules ranging from 4 to 15 atoms that extend to high energies and a large span of configurations.
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Affiliation(s)
- Joel M. Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Chen Qu
- Independent Researcher, Toronto, Canada
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Paul L. Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
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46
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Cao L, Zeng J, Wang B, Zhu T, Zhang JZH. Ab initio neural network MD simulation of thermal decomposition of a high energy material CL-20/TNT. Phys Chem Chem Phys 2022; 24:11801-11811. [PMID: 35506927 DOI: 10.1039/d2cp00710j] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane, also known as HNIW) is one of the most powerful energetic materials. However, its high sensitivity to environmental stimuli greatly reduces its safety and severely limits its application. In this work, ab initio based neural network potential (NNP) energy surfaces for both β-CL-20 and CL-20/TNT co-crystals were constructed. To accurately simulate the thermal decomposition processes of these two crystal systems, reactive molecular dynamics simulations based on the NNPs were performed. Many important intermediate species and their associated reaction paths during the decomposition had been identified in the simulations and the direct results on detonation temperatures of both systems were provided. The simulations also showed clearly that 2,4,6-trinitrotoluene (TNT) molecules in the co-crystal act as a buffer to slow down the chain reactions triggered by nitrogen dioxide and this effect is more significant at lower temperatures. Specifically, the addition of TNT molecules in the CL-20/TNT co-crystal introduces intermolecular hydrogen bonds between CL-20 and TNT molecules in the system, thereby increasing the thermal stability of the co-crystal. The current reactive molecular dynamics simulation is performed based on the NNP which helps in accelerating the speed of ab initio molecular dynamics (AIMD) simulation by more than 3 orders of magnitude while preserving the accuracy of density functional theory (DFT) calculations. This enabled us to perform longer-time simulations at more realistic temperatures that traditional AIMD methods cannot achieve. With the advantage of the NNP in its powerful fitting ability and transferability, the NNP-based MD simulation can be widely applied to energetic material systems.
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Affiliation(s)
- Liqun Cao
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
| | - Jinzhe Zeng
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway 08854-8076, NJ, USA
| | - Bo Wang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
| | - Tong Zhu
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China. .,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
| | - John Z H Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China. .,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China.,Department of Chemistry, New York University, New York 10003, USA.,Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.,Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi, 030006, China
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Houston PL, Qu C, Nandi A, Conte R, Yu Q, Bowman JM. Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods. J Chem Phys 2022; 156:044120. [DOI: 10.1063/5.0080506] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
- Paul L. Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA and Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Chen Qu
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06511, USA
| | - Joel M. Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
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Theoretical Description of Water from Single-Molecule to Condensed Phase: a Review of Recent Progress on Potential Energy Surfaces and Molecular Dynamics. CHINESE J CHEM PHYS 2022. [DOI: 10.1063/1674-0068/cjcp2201005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Lin S, Peng D, Yang W, Gu FL, Lan Z. Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface. J Chem Phys 2021; 155:214105. [PMID: 34879677 PMCID: PMC8654486 DOI: 10.1063/5.0067176] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 11/09/2021] [Indexed: 11/15/2022] Open
Abstract
The H-atom dissociation of formaldehyde on the lowest triplet state (T1) is studied by quasi-classical molecular dynamic simulations on the high-dimensional machine-learning potential energy surface (PES) model. An atomic-energy based deep-learning neural network (NN) is used to represent the PES function, and the weighted atom-centered symmetry functions are employed as inputs of the NN model to satisfy the translational, rotational, and permutational symmetries, and to capture the geometry features of each atom and its individual chemical environment. Several standard technical tricks are used in the construction of NN-PES, which includes the application of clustering algorithm in the formation of the training dataset, the examination of the reliability of the NN-PES model by different fitted NN models, and the detection of the out-of-confidence region by the confidence interval of the training dataset. The accuracy of the full-dimensional NN-PES model is examined by two benchmark calculations with respect to ab initio data. Both the NN and electronic-structure calculations give a similar H-atom dissociation reaction pathway on the T1 state in the intrinsic reaction coordinate analysis. The small-scaled trial dynamics simulations based on NN-PES and ab initio PES give highly consistent results. After confirming the accuracy of the NN-PES, a large number of trajectories are calculated in the quasi-classical dynamics, which allows us to get a better understanding of the T1-driven H-atom dissociation dynamics efficiently. Particularly, the dynamics simulations from different initial conditions can be easily simulated with a rather low computational cost. The influence of the mode-specific vibrational excitations on the H-atom dissociation dynamics driven by the T1 state is explored. The results show that the vibrational excitations on symmetric C-H stretching, asymmetric C-H stretching, and C=O stretching motions always enhance the H-atom dissociation probability obviously.
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Affiliation(s)
| | | | - Weitao Yang
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - Feng Long Gu
- Authors to whom correspondence should be addressed: and
| | - Zhenggang Lan
- Authors to whom correspondence should be addressed: and
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