Are Universal Potentials Ready for Alkali-Ion Battery Kinetics?

TL;DR

This study benchmarks various universal machine learning interatomic potentials for alkali-ion battery materials, revealing their strengths and limitations in predicting atomic-scale kinetics and emphasizing the importance of training data diversity.

Contribution

It systematically evaluates state-of-the-art uMLIPs against DFT benchmarks, highlighting the impact of architecture and training data on kinetic prediction accuracy.

Findings

Orb-v3 excels in static migration barrier predictions.

GRACE model accurately reproduces ion diffusivities.

High-temperature, non-equilibrium data improves kinetic modeling.

Abstract

Accelerating alkali-ion battery discovery requires accurate modeling of atomic-scale kinetics, yet the reliability of universal machine learning interatomic potentials (uMLIPs) in capturing these high-energy landscapes remains uncertain. Here, we systematically benchmark state-of-the-art uMLIPs, including M3GNet, CHGNet, MACE, SevenNet, GRACE, and Orb, against DFT baselines for cathodes and solid electrolytes. We find that the Orb-v3 family excels in static migration barrier predictions (MAE $\approx$ 75--111 meV), driven primarily by architectural refinements. Conversely, for dynamic transport, the GRACE model trained on the OMat24 dataset demonstrates superior fidelity in reproducing ion diffusivities and structural correlations. Our results reveal that while architectural sophistication (e.g., equivariance) is beneficial, the inclusion of high-temperature, non-equilibrium training data is the dominant driver of kinetic accuracy. These findings establish that modern uMLIPs are sufficiently robust to serve as zero-shot surrogates for high-throughput kinetic screening of next-generation energy storage materials.