Origins of Universal Machine Learning Force-Field Errors in Multicomponent Materials
Universal machine learning force-field generalization to multicomponent environments generated by compositional design remains insufficiently assessed. We construct a benchmark of 7,599 multicomponent configurations inspired by high-entropy design, elemental substitution and anion mixing. Eleven pretrained models are evaluated against density functional theory for energies, forces and stresses, with assessment extended to elastic, vibrational and adsorption-related properties. Force errors are analysed through training-reference coverage, local geometric heterogeneity, distance directionality and elemental response. Distances to training-reference environments reveal a qualitative associa...
| Authors | Hongwei Du, Dingyang Lv, Baole Wei et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 7 October 2026 |
| Last updated at source | 8 October 2026 |
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