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Liu C, Aguirre NF, Cawkwell MJ, Batista ER, Yang P. Efficient Parameterization of Density Functional Tight-Binding for 5 f-Elements: A Th-O Case Study. J Chem Theory Comput 2024; 20:5923-5936. [PMID: 38990696 PMCID: PMC11270830 DOI: 10.1021/acs.jctc.4c00145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/23/2024] [Accepted: 06/26/2024] [Indexed: 07/13/2024]
Abstract
Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th-O system, which was subsequently applied to the study of ThO2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO2. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.
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Affiliation(s)
- Chang Liu
- Theoretical Division, Los Alamos
National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Néstor F. Aguirre
- Theoretical Division, Los Alamos
National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Marc J. Cawkwell
- Theoretical Division, Los Alamos
National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Enrique R. Batista
- Theoretical Division, Los Alamos
National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ping Yang
- Theoretical Division, Los Alamos
National Laboratory, Los Alamos, New Mexico 87545, United States
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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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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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Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MM, Hu F, Huang J, Krämer A, Michel J, Mitchell JA, Pande VS, Rodrigues JPGLM, Rodriguez-Guerra J, Simmonett AC, Singh S, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, De Fabritiis G, Markland TE. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials. J Phys Chem B 2024; 128:109-116. [PMID: 38154096 PMCID: PMC10846090 DOI: 10.1021/acs.jpcb.3c06662] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Raimondas Galvelis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Raúl P. Peláez
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
| | - Charlles R. A. Abreu
- Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro 68542, Brazil
- Redesign Science Inc., 180 Varick St., New York, NY 10014, USA
| | - Stephen E. Farr
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Emilio Gallicchio
- Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, NY, USA
- Ph.D. Program in Chemistry and Ph.D. Program in Biochemistry, The Graduate Center of the City University of New York, New York, NY, USA
| | - Anton Gorenko
- Stream HPC, Koningin Wilhelminaplein 1 - 40601, 1062 HG Amsterdam, Netherlands
| | - Michael M. Henry
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Frank Hu
- Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
| | - Andreas Krämer
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin, Germany
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom
| | - Joshua A. Mitchell
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, CA 95616, USA
| | - Vijay S. Pande
- Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, CA 94025, USA
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - João PGLM Rodrigues
- Department of Structural Biology, Stanford University, Stanford, CA 94305, USA
| | - Jaime Rodriguez-Guerra
- Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117 Berlin, Germany
| | - Andrew C. Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sukrit Singh
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Jason Swails
- Entos Inc., 9310 Athena Circle, La Jolla, CA 92037, USA
| | - Philip Turner
- College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Yuanqing Wang
- Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24 Waverly Place, New York, NY 10004, USA
| | - Ivy Zhang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
| | - Gianni De Fabritiis
- Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain
- ICREA, Passeig Lluis Companys 23, 08010, Barcelona, Spain
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