Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects

TL;DR

This paper demonstrates that an adaptive parareal algorithm combining machine-learned and empirical force fields can significantly accelerate molecular dynamics simulations of atomistic defects while maintaining statistical accuracy.

Contribution

It introduces an adaptive parareal algorithm that integrates machine-learning potentials with empirical potentials for efficient Langevin dynamics simulations.

Findings

Significant computational speedups achieved.

Statistical accuracy maintained without full trajectorial accuracy.

Effective for simulating defect dynamics in tungsten.

Abstract

We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [F. Legoll, T. Lelievre and U. Sharma, SISC 2022]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics. We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy.