Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

Abstract

Fast, and accurate prediction of ionic migration barriers (Em) is crucial for designing next-generation battery materials that combine high energy density with facile ion transport. Given the computational costs associated with estimating E_m using conventional density functional theory (DFT) based nudged elastic band (NEB) calculations, we benchmark the accuracy in E_m and geometry predictions of five foundational machine learned interatomic potentials (MLIPs), which can potentially accelerate predictions of ionic transport. Specifically, we assess the accuracy of MACE-MP-0, Orb-v3, SevenNet, CHGNet, and M3GNet models, coupled with the NEB framework, against DFT-NEB-calculated E_m across a diverse set of battery-relevant chemistries and structures. Notably, MACE-MP-0 and Orb-v3 exhibit the lowest mean absolute errors in E_m predictions across the entire dataset and over datapoints that are not outliers, respectively. Importantly, Orb-v3 and SevenNet classify ‘good’ versus ‘bad’ ionic conductors with an accuracy of >82%, based on a threshold E_m of 500 meV, indicating their utility in high-throughput screening approaches. Notably, intermediate images generated by MACE-MP-0 and SevenNet provide better initial guesses relative to conventional interpolation techniques in >71% of structures, offering a practical route to accelerate subsequent DFT-NEB relaxations. Finally, we observe that accurate E_m predictions by MLIPs are not correlated with accurate (local) geometry predictions. Our work establishes the use-cases, accuracies, and limitations of foundational MLIPs in estimating E_m and should serve as a base for accelerating the discovery of novel ionic conductors for batteries and beyond.