Issue 9, 2023

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

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.

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

Supplementary files

Article information

Article type
Communication
Submitted
06 Gen. 2023
Accepted
08 Mezh. 2023
First published
23 Mezh. 2023

Mater. Horiz., 2023,10, 3416-3428

Author version available

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

H. Liu, Z. Huang, S. S. Schoenholz, E. D. Cubuk, M. M. Smedskjaer, Y. Sun, W. Wang and M. Bauchy, Mater. Horiz., 2023, 10, 3416 DOI: 10.1039/D3MH00028A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements