Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

TL;DR

This study benchmarks five machine learned interatomic potentials for predicting ionic migration barriers, highlighting their accuracy, classification ability, and practical utility in battery material discovery.

Contribution

It provides a comprehensive assessment of foundational MLIPs for migration barrier prediction, identifying their strengths and limitations for accelerating battery material research.

Findings

MACE-MP-0 and Orb-v3 have the lowest mean absolute errors in $E_m$ predictions.

Orb-v3 and SevenNet classify conductors with over 82% accuracy.

Intermediate images from MACE-MP-0 and SevenNet improve initial guesses for DFT-NEB calculations.

Abstract

Fast, and accurate prediction of ionic migration barriers ($E_m$) 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 data points 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.