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Chen et al. J Mater Inf 2022;2:19 https://dx.doi.org/10.20517/jmi.2022.23 Page 11 of 21

Figure 7. Illustration of the steps incorporated into the integrated HT-READ methodology. Clockwise from the top left, computational
screening utilizing CALPHAD and the ML model provides recommendations for sample library compositions. The samples are then
synthesized, processed, characterized, tested and analyzed in an automated HT fashion. New data are utilized to improve the
subsequent screening and design. Reproduced with permission [77] . Copyright 2021, Elsevier. CALPHAD: calculations of phase diagrams;
SEM: scanning electron microscopy; XRD: X-ray diffraction; HT-READ: HT rapid experimental alloy development; HT: high-throughput.

The co-sputtering method has shown significant potential for the HT synthesis of HEAs due to the small
grain size, few defects and low contamination in film samples [74,79] . More importantly, this HT co-sputtering
approach has been utilized successfully to synthesize HEA catalysts and build a library. The Ludwig group
demonstrated a closed-loop, data-driven HT experimentation technique that iteratively combines DFT
[80]
calculations, the combinatorial synthesis of material libraries and HT characterization [Figure 8E] . In this
work, three Ag-Ir-Pd-Pt-Ru material libraries with large compositional spaces, centered around the
predicted compositions, were prepared by combinatorial co-sputtering of the five elemental targets. The
refined model, with the input derived from HT characterization data sets, could predict the activity of the
exemplary Ag-Ir-Pd-Pt-Ru model system and further identify optimal HEA catalysts in an unprecedented
manner. Furthermore, they extended this strategy to the HEA systems of Rh-Ir-Pd-Pt-Ru to unravel the
[81]
composition-activity-stability relations in HEA electrocatalysts .

ML MODELS FOR HEA CATALYSTS
ML is a powerful tool for accelerating catalyst discovery, as it can be used to build models with high
accuracy, predict the catalytic performance of unknown catalysts and understand the
structure-property-performance relationships, especially for HEA catalysts with huge compositional
[82]
spaces . The key to successful ML models is to use suitable general descriptors, which can accurately and
comprehensively represent the structural information of the catalysts. An effective descriptor can accelerate
[83]
the development of ML models and uncover the fundamental physical nature of the catalytic process . In
the review, we briefly summarize the ML models with the descriptors developed in the field of HEA
catalysts, which mainly include the following four reactions of ammonia decomposition and synthesis, the
ORR and the CO RR.
2

ML models of ammonia decomposition and synthesis on HEA catalysts
Based on HT calculations, a database with 1911 configurations was generated by Saidi et al., where the
[56]
adsorption energies of N* were within the range of -2.4-1.2 eV on CoMoFeNiCu HEAs . The large