Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Comptes Rendus. Mécanique Year : 2023

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

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.

Dates and versions

hal-03909092 , version 1 (21-12-2022)

Identifiers

Cite

Olga Gorynina, Frédéric Legoll, Tony Lelièvre, Danny Perez. Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects. Comptes Rendus. Mécanique, 2023, 351 (S1), pp.479-503. ⟨10.5802/crmeca.220⟩. ⟨hal-03909092⟩
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