High-entropy alloys, typically with 4 or more unique elements with varying compositions and competing crystal structures, have extremely large design spaces for unique chemical and mechanical properties. To accelerate computational design, a metaheuristic Hybrid Cuckoo Search (CS) optimization algorithm was developed to construct on-the-fly alloy configurational models having targeted atomic site and pair probabilities on arbitrary crystal lattices, represented by Super-Cell Random APproximates (SCRAPs) with S number of unique crystallographic sites. The Hybrid CS permits efficient global solutions for large, discrete combinatorial optimization that scale linearly with the number of parallel processors, and linearly in S sites for SCRAPs. For example, a 4-element, 128-site SCRAP is found in seconds rather than days—more than 13,000-fold reduction of times over current strategies using Monte-Carlo methods. This method enables computational alloy design that is currently impractical for multi-component alloys. In this paper, the model is validated and applications to real alloys with targeted atomic short-range order showcased. Being problem-agnostic, our Hybrid CS offers potential applications to improve optimization applications in diverse fields such as manufacturing, commerce, finance, science, and engineering.
“Accelerating Computational Modeling and Design of High-Entropy Alloys,” Rahul Singh, Aayush Sharma, Prashant Singh, Ganesh Balasubramanian, Duane D. Johnson*, Nature Computational Science (2021). DOI: 10.1038/s43588-020-00006-7
See also, https://go.nature.com/3awPtIw Borrowing from birds, optimal model design of high-entropy alloys in seconds