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Mooraj et al. J Mater Inf 2023;3:4 Journal of
DOI: 10.20517/jmi.2022.41
Materials Informatics

Review Open Access

A review on high-throughput development of high-
entropy alloys by combinatorial methods

*
Shahryar Mooraj, Wen Chen
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA.
* Correspondence to: Prof. Wen Chen, Department of Mechanical and Industrial Engineering, University of
Massachusetts Amherst, 160 Governors Drive, Amherst, MA 01003, USA. E-mail: wenchen@umass.edu
How to cite this article: Mooraj S, Chen W. A review on high-throughput development of high-entropy alloys by combinatorial
methods. J Mater Inf 2023;3:4. https://dx.doi.org/10.20517/jmi.2022.41

Received: 8 Dec 2022 First Decision: 13 Jan 2023 Revised: 6 Feb 2023 Accepted: 7 Mar 2023 Published: 17 Mar 2023
Academic Editors: Tong-Yi Zhang, Xingjun Liu, Yong Yang Copy Editor: Ke-Cui Yang Production Editor: Ke-Cui Yang

Abstract
High-entropy alloys (HEAs) are an emerging class of alloys with multi-principal elements that greatly expands the
compositional space for advanced alloy design. Besides chemistry, processing history can also affect the phase and
microstructure formation in HEAs. The number of possible alloy compositions and processing paths gives rise to
enormous material design space, which makes it challenging to explore by traditional trial-and-error approaches.
This review highlights the progress in combinatorial high-throughput studies towards rapid prediction,
manufacturing, and characterization of promising HEA compositions. This review begins with an introduction to
HEAs and their unique properties. Then, this review describes high-throughput computational methods such as
machine learning that can predict desired alloy compositions from hundreds or even thousands of candidates. The
next section presents advances in combinatorial synthesis of material libraries by additive manufacturing for
efficient development of high-performance HEAs at bulk scale. The final section discusses the high-throughput
characterization techniques used to accelerate the material property measurements for systematic understanding
of the composition-processing-structure-property relationships in combinatorial HEA libraries.

Keywords: High-entropy alloys, machine learning, combinatorial studies, high throughput, additive manufacturing,
alloy design

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
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