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Graph Neural Networks Accelerated Molecular Dynamics by Zijie Li; Kazem Meidani; Prakarsh Yadav; Amir Barati Farimani is a Physics and Astronomy article available to read on EtoBox.

What is Graph Neural Networks Accelerated Molecular Dynamics about?

Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long timescale simulations with femtosecond integration is very expensive. In each MD step, numerous iterative computations are performed to calculate energy based on different types of interaction and their corresponding spatial gradients. These repetitive computations can be learned and surrogated by a deep learning model, such as a Graph Neural Network (GNN). In this work, we developed a GNN Accelerated MD (GAMD) model that directly predicts forces, given the state of the system (atom positions, atom types), bypassing the evaluation of potential energy. By training the GNN on a variety of data sources (simulation data derived from classical MD and density functional theory), we show that GAMD can predict the dynamics of two typical molecular systems, Lennard-Jones system and water system, in the NVT ensemble with velocities regulated by a thermostat. We further show that GAMD's learning and inference are agnostic to the scale, where it can scale to much larger systems at test time. We also perform a comprehensive benc

Who reads Graph Neural Networks Accelerated Molecular Dynamics?

Author

Zijie Li; Kazem Meidani; Prakarsh Yadav; Amir Barati Farimani

Publisher

AIP Publishing

Published

2022

Language

EN

Field

Physics and Astronomy (Physical Sciences)