Authors
Killian Sheriff, Daniel Z Xiao, Yifan Cao, Lewis R Owen, Rodrigo Freitas
Published in
Science advances. Volume 12. Issue 25. Pages eaea9951. Jun 19, 2026. Epub Jun 19, 2026.
Abstract
Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered (i.e., stoichiometric compounds) to completely disordered (i.e., solid solutions). Accurately capturing this range of chemical arrangements remains a major challenge, limiting the predictive accuracy of machine learning potentials (MLPs) in materials modeling. Here, we combine information theory and machine learning to optimize the sampling of chemical motifs and design MLPs that effectively capture the behavior of metallic alloys across their entire compositional and structural landscape. The effectiveness of this approach is demonstrated by predicting the compositional dependence of various materials properties-including stacking-fault energies, short-range order, heat capacities, and phase diagrams-for the AuPt and CuAu binary alloys, the ternary CrCoNi, and the TiTaVW high-entropy alloy. Extensive comparison against experimental data demonstrates the robustness of this approach in enabling materials modeling with high physical fidelity.
PMID:
42319938
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.
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