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Interatomic-Potential-Free, Data-Driven Molecular Dynamics
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DOI：10.1016/j.cma.2023.116224.png)
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摘要
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We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e. g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i. e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword:
Data-Driven computing
Molecular dynamics
Optimal control
Game theory
Wasserstein metric
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期刊
IF:
7.3
论文数:
1.3W
被引数:
5.6W
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