Understanding and mitigating distribution shifts for universal machine learning interatomic potentials

Abstract

We find common distribution shifts that pose challenges for universal machine learning interatomic potentials (MLIPs). We develop test-time refinement strategies that mitigate the shifts and provide insights into why MLIPs struggle to generalize.