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

We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is si...

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Bibliographic Details
Published in:	Comptes rendus. Mecanique Vol. 351; no. S1; pp. 479 - 503
Main Authors:	Gorynina, Olga, Legoll, Frédéric, Lelièvre, Tony, Perez, Danny
Format:	Journal Article
Language:	English
Published:	United States Académie des sciences (Paris) 01.01.2023 Academie des Sciences Académie des sciences
Subjects:	Adaptive algorithm MATERIALS SCIENCE Mathematics Molecular dynamics Numerical Analysis Parallel-in-time simulation Statistical accuracy
ISSN:	1631-0721, 1873-7234, 1873-7234
Online Access:	Get full text
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Summary:	We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. 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.
Bibliography:	USDOE 89233218CNA000001 LA-UR-22-33127
ISSN:	1631-0721 1873-7234 1873-7234
DOI:	10.5802/crmeca.220