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Chen et al. J Mater Inf 2022;2:19 https://dx.doi.org/10.20517/jmi.2022.23 Page 7 of 21

Figure 4. Adsorption energy affected by combined ligand and coordination effects. (A) Different coordination environments are ranked
in increasing order of total coordination number of nearest neighbors, as defined in the text. (B) Frequency distribution of OH*
adsorption energy for each coordination environment (left), whose mean values are found to correlate linearly with the total
coordination number of nearest neighbors (middle). The coordination types are ordered the same manner in (A) and (B) and the
[61]
energy values are horizontally aligned in (B). Reproduced with permission . Copyright 2020, Elsevier. MAE: mean absolute error;
RMSE: root-mean-square error.

neural network techniques are powerful tools that can be used to overcome the huge chemical space of HEA
catalysts.

HT experimental approach
HT theoretical calculations have shown significant potential in generating a database and predicting novel
HEA catalysts with desirable catalytic performance. Most HT theoretical calculation techniques, however,
utilize semi-empirical physical and chemical parameters without considering the complexity of such
[62]
experiments . The synthesis, characterization and performance testing severely affect the reliability of
theoretical calculation results. Therefore, the HT experimental approaches should also be explored to assess
the structure, property and performance of HEA catalysts, which could further validate the HT theoretical
[63]
calculation results .

For example, Shukla et al. developed a solid-state gradient alloying method for the HT screening of HEA
systems, in which a tapered section of a pure alloying element was retrofitted to the base alloy groove via
milling and the subsequent friction stir processing of the assembled region achieved a continuous increase
[64]
in alloy content of the additional element, as shown in Figure 5A-D . This prototype technique was
applied to investigate the effect of the gradient variations of Cu on the phases (ε-hcp and γ-fcc) and the
mechanical property response of a Fe Mn Co Cr Si HEA. HEA samples with a compositional gradient
40
20
20
5
15
can also be achieved by diffusion multiple technologies. Zhu et al. applied this technology to the HT
[65]
synthesis of a Ti-based HEA . By combining the HT diffusion multiple technology and back propagation
neural network, a Ti alloy was successfully designed and synthesized, which showed outstanding mechanical
properties. HEA products with a continuous composition gradient achieved by HT experiments can be