Predicting Properties of Amorphous Solids with Graph Network Potentials

Abstract

Graph neural networks (GNNs) provide an architecture consistent with the physical nature of molecules and crystals, and have proven capable of efficiently learning their properties, particularly from density functional theory (DFT) calculations. When used in atomistic modeling, general-purpose GNNs can unlock new areas of research in materials science and chemistry. In this paper, we present an end-to-end molecular dynamics workflow coupled with a large-scale E(3)-equivariant GNN-based general-purpose interatomic potential to model amorphous solids in any inorganic chemistry. Using this approach in high-throughput, we predict the structures and energetics of a large number of inorganic binary amorphous systems, with close to 28,800 unique compositions. By comparing the predicted energies of amorphous solids to DFT, we show that general-purpose GNN potentials provide strong zero-shot capability in modeling these systems.