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Chen et al. J Mater Inf 2022;2:19 https://dx.doi.org/10.20517/jmi.2022.23 Page 17 of 21

(3) Current HT calculations are mainly focused on the adsorption of some important intermediates on HEA
catalysts, rather than the most intuitive catalytic performance. This strategy effectively reduces the cost of
computation; however, it only works when the BEP and scaling relationships between the adsorption
energies of the relevant intermediates are still valid on the surface of the HEA catalysts;

(4) Although ML has a powerful prediction ability, the accuracy depends on the sufficiency of training data.
The existing databases contain many valuable material data; unfortunately, there are still more data in the
published literature that cannot be entered into databases and shared. Therefore, a more comprehensive and
generic material information standard should be established to achieve data sharing among databases and to
reduce obstacles in data acquisition;

(5) The key challenge in ML is the exploration of suitable, simple and general descriptors to accurately
describe HEA catalysts, which are required to reasonably design catalysts and efficiently screen candidates;

(6) The prediction ability of ML models based on HT techniques has been proven to be powerful. Another
important milestone is to uncover the structure-property-performance relationships of HEA catalysts and
explore new mechanisms in the catalysis field, which deserves more attention in future research.

PERSPECTIVES
To address the above challenges, more HT synthesis strategies, as well as HT techniques for characterizing
and testing catalytic performances, should be explored to build databases for HEA catalysts. The achieved
databases should be more comprehensive, realistic, reliable and universal. In addition to the composition of
HEA catalysts, we should also pay attention to other parameters (such as morphology, size, specific surface
area and reaction environments), which are directly related to catalytic performances. For HT calculations,
a more comprehensive list of catalytic performance parameters should be considered to evaluate the
catalytic performance of HEA catalysts, rather than only calculating the adsorption energy of some
important intermediates. Because both the BEP relationship and scaling relationship of adsorption energies
are currently controversial phenomena for HEA catalysts, which need to be further verified. With their
complex surface active sites, HEA catalysts have shown excellent catalytic activity for various catalytic
reactions. The selectivity of HEA catalysts, however, is also a concern as these complex active centers might
catalyze multiple reactions, which is another challenge for the development of HEA catalysts. To make full
use of the existing data (both experimental and calculation data) in published studies and databases, and
avoid the batch effect, workflows with natural language processing techniques should be explored to achieve
effective communication between humans and computers with human languages and integrate these useful
data into a comprehensive database. Finally, more effective descriptors should be proposed, as the accuracy
and universality of the designed ML models are determined by the descriptors adopted. An effective
descriptor can accelerate our understanding of HEA catalysts and help us to discover new catalytic
mechanisms, thus promoting the development of HEA catalysts. We hope that this review will help
researchers to better understand the significance of HT techniques and ML models for the development of
HEA catalysts.

DECLARATIONS
Authors’ contributions
Made substantial contributions to conception and design of this review, writing and editing: Chen L, Singh
CV, Zou Y
Made substantial contributions to collation of literatures, figures preparation, and writing: Chen L, Chen Z,
Singh CV, Zou Y