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Chen et al. J Mater Inf 2022;2:19 Journal of
DOI: 10.20517/jmi.2022.23
Materials Informatics

Review Open Access

High-entropy alloy catalysts: high-throughput and
machine learning-driven design

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Lixin Chen , Zhiwen Chen , Xue Yao , Baoxian Su , Weijian Chen , Xin Pang , Keun-Su Kim , Chandra Veer
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Singh , Yu Zou 1*
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Department of Materials Science and Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada.
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CanmetMATERIALS, Energy Technology Sector (ETS), Natural Resources Canada, Hamilton, L8P 0A5, Canada.
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Security and Disruptive Technologies Centre, National Research Council Canada, Ottawa, K1A 0R6, Canada.
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Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada.
* Correspondence to: Prof. Chandra Veer Singh, Department of Materials Science and Engineering, University of Toronto, 184
College Street, Toronto, ON M5S 3E4, Canada. E-mail: chandraveer.singh@utoronto.ca; Prof. Yu Zou, Department of Materials
Science and Engineering, University of Toronto, 184 College Street, Toronto, ON M5S 3E4, Canada. E-mail: mse.zou@utoronto.ca
How to cite this article: Chen L, Chen Z, Yao X, Su B, Chen W, Pang X, Kim KS, Singh CV, Zou Y. High-entropy alloy catalysts:
high-throughput and machine learning-driven design. J Mater Inf 2022;2:19. https://dx.doi.org/10.20517/jmi.2022.23
Received: 6 Aug 2022 First Decision: 13 Sep 2022 Revised: 12 Oct 2022 Accepted: 9 Nov 2022 Published: 22 Nov 2022
Academic Editors: Xingjun Liu, Wen Chen, Yongjie Hu Copy Editor: Ke-Cui Yang Production Editor: Ke-Cui Yang
Abstract
High-entropy alloy (HEA) catalysts have recently attracted worldwide research interest due to their promising
catalytic performance. Most current studies focus on designing HEA catalysts through trial-and-error methods.
This produces scattered data and is not conducive to obtaining a fundamental understanding of the
structure-property-performance relationships for HEA catalysts, thereby hindering their rational design.
High-throughput (HT) techniques and machine learning (ML) methods show significant potential in generating,
processing and analyzing databases with a vast amount of data, providing a new strategy for the further
development of HEA catalysts. In this review, we summarize the recent literature on HT techniques for HEA
synthesis, characterization and performance testing. We also review the ML models that are used to process and
analyze existing databases to accelerate the discovery of HEA catalysts. Finally, the potential challenges and
perspectives of HT techniques and ML models are presented to accelerate the discovery of new HEA catalysts and
promote their development.

Keywords: High-entropy alloys, catalysts, high-throughput, machine learning, structure-activity relationship

© The Author(s) 2022. 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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