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Page 16 of 21 Chen et al. J Mater Inf 2022;2:19 https://dx.doi.org/10.20517/jmi.2022.23

Figure 11. New research strategy for HEA catalysts. HT experimental and theoretical methods are used to generate comprehensive
databases of HEA catalysts. Highly transferable and accurate ML models are explored to analyze databases and predict optimal HEA
catalysts. New insights into active centers and new catalytic mechanisms and descriptors are expected to be developed on the basis of
HT techniques and ML models. Finally, high-performance HEA catalysts will be rationally designed, promoting the development of
catalysis. Reproduced with permission [25] . Copyright 2022, Elsevier. CE: counter electrode; RE: reference electrode; WE: working
electrode; HEA: high-entropy alloy; HT: high-throughput; ML: machine learning.

CHALLENGES
HEA catalyst research is in its infancy and some open questions for synthetic methods, catalytic reactions
and mechanistic understandings should be addressed. As the number of components increases, the active
centers of HEAs become much more complex compared to traditional alloy systems. Thus, the key study in
the research of HEA catalysts is the identification of active centers. The development of HT techniques and
ML models is an essential part of the accelerated research of HEA catalysts with a huge compositional space,
[25]
as illustrated in Figure 11 . However, more challenges are still unresolved for the development of HT
techniques and ML models for HEA catalysts, as shown below:

(1) The morphology and particle size of HEA catalysts should have obvious influences on the catalytic
performance; however, they are difficult to control flexibly by current HT synthesis techniques;

(2) Though many HT synthesis techniques have been successful, the synthesized HEAs are not easy to
further test for catalytic performance;