Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator

Han Liu*, Zijie Huang, Samuel S. Schoenholz, Ekin D. Cubuk, Morten M. Smedskjaer, Yizhou Sun, Wei Wang, Mathieu Bauchy*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Many-body dynamics of atoms such as glass dynamics is generally governed by complex (and sometimes unknown) physics laws. This challenges the construction of atom dynamics simulations that both (i) capture the physics laws and (ii) run with little computation cost. Here, based on graph neural network (GNN), we introduce an observation-based graph network (OGN) framework to “bypass all physics laws” to simulate complex glass dynamics solely from their static structure. By taking the example of molecular dynamics (MD) simulations, we successfully apply the OGN to predict atom trajectories evolving up to a few hundred timesteps and ranging over different families of complex atomistic systems, which implies that the atom dynamics is largely encoded in their static structure in disordered phases and, furthermore, allows us to explore the capacity of OGN simulations that is potentially generic to many-body dynamics. Importantly, unlike traditional numerical simulations, the OGN simulations bypass the numerical constraint of small integration timestep by a multiplier of ≥5 to conserve energy and momentum until hundreds of timesteps, thus leapfrogging the execution speed of MD simulations for a modest timescale.

Original languageEnglish
JournalMaterials Horizons
Volume10
Issue number9
Pages (from-to)3416-3428
Number of pages13
ISSN2051-6347
DOIs
Publication statusPublished - 23 Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 The Royal Society of Chemistry.

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