Interatomic-Potential-Free, Data-Driven Molecular Dynamics

TL;DR

This paper introduces a data-driven approach to molecular dynamics that directly uses sampled force data from ab initio calculations, eliminating the need for empirical potentials and demonstrating robust convergence and applicability.

Contribution

It proposes a novel data-driven molecular dynamics framework that bypasses traditional potential modeling, with provable convergence and demonstrated effectiveness on complex molecular systems.

Findings

Proves convergence of the data-driven MD algorithms.

Demonstrates applicability to C60 buckminsterfullerenes.

Shows robustness with respect to data quality and quantity.

Abstract

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.