Motif-based sampling of training configurations produces machine learning potentials that accurately predict alloy properties across compositions, as validated against experiments for phase diagrams, melting, short-range order, thermal expansion, and heat capacity.
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Machine learning potentials for modeling alloys across compositions
Motif-based sampling of training configurations produces machine learning potentials that accurately predict alloy properties across compositions, as validated against experiments for phase diagrams, melting, short-range order, thermal expansion, and heat capacity.