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We numerically investigate an adaptive version of the parareal algorithm inthe context of molecular dynamics. This adaptive variant has been originallyintroduced in [F. Legoll, T. Lelievre and U. Sharma, SISC 2022]. We focus hereon test cases of physical interest where the dynamics of the system is modelledby the Langevin equation and is simulated using the molecular dynamics softwareLAMMPS. In this work, the parareal algorithm uses a family of machine-learningspectral neighbor analysis potentials (SNAP) as fine, reference, potentials andembedded-atom method potentials (EAM) as coarse potentials. We consider aself-interstitial atom in a tungsten lattice and compute the average residencetime of the system in metastable states. Our numerical results demonstratesignificant computational gains using the adaptive parareal algorithm incomparison to a sequential integration of the Langevin dynamics. We alsoidentify a large regime of numerical parameters for which statistical accuracyis reached without being a consequence of trajectorial accuracy.

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