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Chen et al. J Mater Inf 2022;2:19 https://dx.doi.org/10.20517/jmi.2022.23 Page 3 of 21

Figure 1. Number of publications on HEAs, HEA catalysts and HEA machine learning (ML) by 2021. The data are based on the Web of
Science. HEA: high-entropy alloy.

understanding HEA catalysts can the rational design of HEA catalysts be implemented to make a
breakthrough in catalysis.

To address the above challenge, more data are needed to analyze the structure-property-performance
relationships of HEA catalysts. However, it is difficult to compile the single data points achieved by
traditional “trial-and-error” methods and “directed research” approaches into an effective database due to
the variance in synthesis methods, particle sizes, morphologies, and so on. Moreover, it is prohibitively
time-consuming and costly to collect the data points one by one. To solve this issue, high-throughput (HT)
[26]
techniques have been proposed to generate comprehensive databases with high efficiency . With the
enhancement of computing power and the continuous improvement of theoretical calculation methods, HT
theoretical calculations play an essential role in building databases as a result of their fast speed and low cost
when compared with HT experiments. This does not mean that HT experiments are unnecessary, as they
are essential to building a realistic database. To better understand HEA catalysts, a large database is a
prerequisite and rational analysis of these data is indispensable. The diversity of HEA catalysts, however,
leads to complex databases, which are difficult to analyze using only physical and chemical knowledge.

Fortunately, machine learning (ML) has received significant attention in recent years as a powerful tool for
processing complex data [27-30] . For example, to understand the structure-activity relationships of SACs for
nitrogen fixation, different 2D materials supporting SACs and boron-doped graphene SACs were explored
using HT density functional theory (DFT) calculations and ML [28,29] . Deng et al. applied DFT calculations
[30]
and ML to develop bi-atom catalysts for the ORR . These ML models provide new insights into atomic
catalysts and help to speed up the design and discovery of new atomic catalysts. HEA catalysts are much
more complex than atomic catalysts and the HT and ML methods show significant promise in exploring the
structure-property-activity relationships of HEA catalysts, as evidenced by the works of Wan et al. and
[31,32]
Roy et al. . These authors investigated IrPtRuRhAg and CuCoNiZnSn HEA catalysts by ML for the ORR
and CO RR, respectively, providing rational guidance for the design of highly efficient HEA catalysts by
2
altering the component elements and composition ratio. Unfortunately, the application of ML in the field of
HEAs is not universal, as shown in Figure 1. We believe that ML could become the most powerful research
tool for the study of HEAs.