Accelerating computational modeling and design of high-entropy alloys

TL;DR

This paper introduces a hybrid Cuckoo Search algorithm that significantly accelerates the computational design of high-entropy alloys by efficiently exploring vast design spaces and generating atomic configurations rapidly.

Contribution

The paper presents a novel hybrid-CS method that overcomes combinatorial optimization challenges, enabling ultrafast, scalable alloy design with arbitrary lattice structures.

Findings

Achieves 13,000+ times faster solutions than existing methods

Scales linearly with system size and efficiently handles large design spaces

Successfully applied to real alloys with various short-range orders

Abstract

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.