Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

TL;DR

GNNFF is a graph neural network framework that accurately predicts atomic forces for molecular dynamics, enabling scalable simulations with high fidelity across various materials, including superionic conductors.

Contribution

This work introduces GNNFF, a novel GNN-based force field that directly predicts forces with high accuracy and efficiency, and demonstrates transferability to larger systems.

Findings

Achieves high force prediction accuracy and computational speed.

Successfully predicts forces for large systems after training on smaller ones.

Accurately simulates Li diffusion in superionic conductor within 14% of AIMD results.

Abstract

Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.