Electrochemical energy storage and conversion systems powered by renewables underpin the clean energy transition, yet sluggish oxygen evolution reaction (OER) severely limits energy efficiency, driving demand for efficient OER catalysts. Perovskites stand out for adjustable electronic structures and strong catalytic activity. High‐entropy perovskites (HEPs), with multiple principal lattice elements, gain stable phases from configurational entropy and favorable electronic states via multicomponent synergy, promising OER performance. However, their vast compositional range renders trial‐and‐error development ineffective, hindering structure–activity analysis and targeted catalyst design. Covering oxide and halide HEPs, this review summarizes advances in HEP OER catalysts over four aspects: synthetic routes, performance regulation, mechanistic analysis via computation, and AI‐driven rational design (covering material screening, descriptor mining, and activity prediction). Unlike prior reviews covering separate high‐entropy oxides, standard perovskites, or general AI catalysis, this work builds a closed logical loop for HEPs spanning synthesis, experiment, mechanism, and rational design. An outlook on AI‐assisted HEP electrocatalysis is offered to inform the development of advanced OER catalytic systems.
The application of high‐entropy intermetallic (HEI) compounds in the field of catalysis has attracted widespread attention, but their huge material space seriously hinders experimental exploration. Herein, we for the first time reported the efficient design, screening, and prediction of a great deal of high‐performance HER catalysts from a huge HEI material space (10
6
) based on our newly established machine learning (ML) driven “decode—describe—design” (3D) framework by experimentally fabricated A
3
B‐type (FeCoNi)
3
(AlTi) system. Over 700 catalysts exhibited better performance than existing experimental results, indicating that the experiment only touched a very small part of the material space. Moreover, we developed various powerful descriptors (such as Λ
OH
, Λ
H
) and analysis tools (such as, RDERA, RPDAD, CPMCC, CPMCS) to decouple the complex interplay of elements into atomic‐ and region‐specific effects, laying the foundation for the establishment of structure‐activity relationships and guiding the rational design of catalysts. The interpretable ML‐driven 3D framework, powerful descriptors, and novel analysis tools enable efficient design and screening, catalytic mechanism elucidation, and structure‐activity relationship establishment. They are expected to stimulate further computational and experimental investigations in related catalyst systems.
Hao Deng, Liming Yang· Advanced Energy Materials· 0 citations
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