Origins of Universal Machine Learning Force-Field Errors in Multicomponent Materials
Researchers tested how well universal machine learning force fields work on complex, multicomponent materials inspired by high-entropy design. They found that errors are not just about training data coverage but also about local geometric complexity and the direction of atomic distances. Compression forces show significantly higher errors than extension forces, revealing a specific challenge in predicting material behavior under stress.