Probing Machine Learning Interatomic Potentials on Ion Transport Properties
TL;DR
A benchmark study systematically benchmark six state‐of‐the‐art MLPs, namely CHGNet, EquiformerV2 in two training variants, MatterSim, SevenNet, and MACE, on representative Li‐ and Na‐based superionic conductors to highlight the importance of accurately capturing atomic forces in reasonably producing ion transport properties in all‐solid‐state battery materials.
Abstract
Machine learning interatomic potentials (MLPs) are promising for accelerating the simulation of ion transport in all‐solid‐state battery materials, but their accuracy across diverse material compositions and symmetries remains unquantified. Here, we systematically benchmark six state‐of‐the‐art MLPs, namely CHGNet, EquiformerV2 in two training variants, MatterSim, SevenNet, and MACE, on representative Li‐ and Na‐based superionic conductors. By comparing predicted atomic forces, diffusion coefficients (DCs) from MLP‐driven molecular dynamics simulations, and second‐ and third‐order interatomic force constants (IFCs) against density functional theory (DFT) and ab initio molecular dynamics (AIMD), we assess the predictive reliability of each model across these properties. EquiformerV2 models, especially the variant trained only on the OMAT dataset, exhibit the lowest force prediction errors and yield DCs in the closest agreement with AIMD. Continually, MLPs with more accurate force predictions produce more reliable diffusion metrics. However, while second‐order IFCs are reasonably captured, all models struggle to reproduce third‐order (anharmonic) IFCs, highlighting significant challenges in modeling anharmonic lattice dynamics with current MLPs. This benchmark study highlights the importance of accurately capturing atomic forces in reasonably producing ion transport properties in all‐solid‐state battery materials and provides practical guidance for universal MLP selection for ion transport simulations and future improvement and development.