CHGNet: Pretrained Neural Network Potential for Fast and Accurate Charge-constrained Molecular Dynamics

Molecular dynamics (MD) simulation coupled on systems with complex electron interactions remains one of the biggest challenges for atomistic modeling. While classical force fields often fail to describe the electronic coupling with ionic rearrangements, the more accurate spin-polarized ab initio molecular dynamics (AIMD) suffer from the computational complexity that prevents long-time and large-scale simulation, which are essential to study ion migrations and phase transformations.

In this work, we present the Crystal Hamiltonian Graph Neural Network (CHGNet) as a novel approach that uses a graph neural network (GNN) based force field to model a universal potential energy surface that can describe both atoms and electrons. CHGNet is pretrained on a large Materials Project Trajectory (MPtrj) Dataset, which consists of over 1 million inorganic structures from over 10 years of density functional theory (DFT) static and relaxation trajectories at the Materials Project. We demonstrate the performance of CHGNet molecular dynamics in Li-ion solid-state electrolyte and phase transformation in Li-ion cathode materials.