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Chen et al. J Mater Inf 2022;2:19 https://dx.doi.org/10.20517/jmi.2022.23 Page 5 of 21

Figure 2. Roadmap of NES generation process. From left to right: an input structure (a template mesh with N sites), a representation of
the N input arrays, the ANNs, a representation of the N output vectors, the atom type associated with each output vector and the
generated configuration. Reproduced with permission [45] . Copyright 2021, AIP Publishing. NES: neural evolution structure.

calculations of surface energies and work functions show significant potential for the application of HEAs in
catalysis because these surface properties are closely related to surface catalysis.

The adsorption energy of a specific intermediate has been considered as a descriptor of the catalytic
performance for the corresponding catalytic reaction. This can be utilized to deliver prominent results due
to the Brønsted-Evans-Polanyi (BEP) relationship between activation barriers and reaction energies and the
scaling relationship among the adsorption energies of some intermediates on catalyst surfaces [49,50] . To
reduce the calculation cost, it is necessary to only consider the adsorption energy of some important
[49]
intermediates to evaluate the catalytic performance of the catalysts . Taking the CO RR as an example,
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various products can be produced at different potentials, while product distributions also depend on the
solvent, promoter and other factors [51,52] . Roy et al. considered that the intermediates of CO*, HCO*, H CO*
2
and H CO* are more important for the formation of methanol from CO , even though several CO RR
3 [53] 2 2
mechanistic pathways are possible for methanol formation . Moreover, the catalyst for the electrocatalytic
CO RR should not be active for the HER and oxidized during the reaction process. Thus, except for CO*,
2
HCO*, H CO* and H CO*, the adsorption energies of H* and O* were calculated by HT DFT calculations.
2
3
The neighboring atoms of the active center on a certain catalyst play a vital role in determining the
adsorption energy of a given intermediate adsorbed on the active center. In this work, the neighboring
atoms were divided into three regions based on their varying effects on the adsorption energy. The first
region is the atoms on the adsorption site; the second region includes the surface layer atoms around the
adsorption site and the third region contains the subsurface layer atoms, which are the nearest neighbors of
the adsorption sites, as shown in Figure 3A-C. A database with 474 data points in more than 40,000 possible
microstructures was generated for the CO RR. Based on this database, the authors built an ML algorithm to
2
predict the adsorption energies of the important intermediates on all possible adsorption sites. This work
demonstrates an efficient approach to exploring catalysts with high catalytic performance, which can be
extended to other reactions in the future.

For ammonia decomposition or synthesis, N*, NH*, NH * and NH * are important intermediates for
2 3
adsorption energy calculations, as well as H*, since the HER is the most important side reaction for
[54,55]
ammonia synthesis . Saidi et al. performed HT DFT calculations to determine the adsorption energies of
[56]
H*, N*, NH*, NH * and NH * species on FeCoNiCuMo HEA surfaces . It was found that the most stable
2
3
adsorption site changes from the HCP site to the bridge site and finally to the top site with increasing H
atoms, as shown in Figure 3D. Based on these adsorption configurations, they established a database with