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arxiv: 2010.12107 · v1 · pith:BG7F3WOHnew · submitted 2020-10-22 · ❄️ cond-mat.mtrl-sci · cs.CE

Accelerating computational modeling and design of high-entropy alloys

classification ❄️ cond-mat.mtrl-sci cs.CE
keywords designalloyscomputationalhigh-entropyhybrid-csschemaacceleratingalloy
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With huge design spaces for unique chemical and mechanical properties, we remove a roadblock to computational design of {high-entropy alloys} using a metaheuristic hybrid Cuckoo Search (CS) for "on-the-fly" construction of Super-Cell Random APproximates (SCRAPs) having targeted atomic site and pair probabilities on arbitrary crystal lattices. Our hybrid-CS schema overcomes large, discrete combinatorial optimization by ultrafast global solutions that scale linearly in system size and strongly in parallel, e.g. a 4-element, 128-atom model [a $10^{73+}$ space] is found in seconds -- a reduction of 13,000+ over current strategies. With model-generation eliminated as a bottleneck, computational alloy design can be performed that is currently impossible or impractical. We showcase the method for real alloys with varying short-range order. Being problem-agnostic, our hybrid-CS schema offers numerous applications in diverse fields.

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