About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
Presentation Title |
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator |
Author(s) |
Mathieu Bauchy |
On-Site Speaker (Planned) |
Mathieu Bauchy |
Abstract Scope |
Molecular dynamics (MD) is a workhorse of computational material science. However, the inner-loop algorithm of MD is computationally expensive. This is a key limitation since, as a result, MD simulations of glasses are typically limited to very fast cooling rates. Here, we introduce a surrogate machine learning simulator that is able to predict the dynamics of liquid glass-forming systems with no prior knowledge of the interatomic potential or nature of the Newton’s law of motion. The surrogate model consists of a graph neural network (GNN) engine that is trained by observing existing MD-generated trajectories. We demonstrate that the surrogate simulator properly predicts the dynamics of a variety of systems featuring very different interatomic interactions. The development of machine-learned surrogate simulators that can effectively replace costly MD simulations could expand the range of space and time scales that are typically accessible to MD simulations. |