Campus Units

Chemical and Biological Engineering, Materials Science and Engineering, Mechanical Engineering, Physics and Astronomy, Ames Laboratory

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

1-2021

Journal or Book Title

Nature Computational Science

Volume

1

Issue

1

First Page

54

Last Page

61

DOI

10.1038/s43588-020-00006-7

Abstract

High-entropy alloys, with N elements and compositions {cν = 1,N} in competing crystal structures, have large design spaces for unique chemical and mechanical properties. Here, to enable computational design, we use a metaheuristic hybrid Cuckoo search (CS) to construct alloy configurational models on the fly that have targeted atomic site and pair probabilities on arbitrary crystal lattices, given by supercell random approximates (SCRAPs) with S sites. Our Hybrid CS permits efficient global solutions for large, discrete combinatorial optimization that scale linearly in a number of parallel processors, and linearly in sites S for SCRAPs. For example, a four-element, 128-site SCRAP is found in seconds—a more than 13,000-fold reduction over current strategies. Our method thus enables computational alloy design that is currently impractical. We qualify the models and showcase application to real alloys with targeted atomic short-range order. Being problem-agnostic, our Hybrid CS offers potential applications in diverse fields.

Comments

This is a post-peer-review, pre-copyedit version of an article published as Singh, Rahul, Aayush Sharma, Prashant Singh, Ganesh Balasubramanian, and Duane D. Johnson. "Accelerating computational modeling and design of high-entropy alloys." Nature Computational Science 1, no. 1 (2021): 54-61. The final authenticated version is available online at DOI: 10.1038/s43588-020-00006-7. Posted with permission.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

Available for download on Friday, January 14, 2022

Published Version

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