Machine Learning‐Guided Design of L1 2 ‐Type Pt‐Based High‐Entropy Intermetallic Compound for Electrocatalytic Hydrogen Evolution

Abstract Rational design of high‐entropy intermetallic compounds (HEICs) remains challenging due to complex structure‐property relationships and the lack of predictive tools. Here, a data‐driven framework is presented to evaluate the hydrogen evolution reaction (HER) activity of L1 2 ‐type quinary Pt 3 M(4) HEICs, where M comprises any four elements from six 3 d transition metals (Cr, Mn, Fe, Co, Ni, Zn). Guided by the Pm‐3m space group, 15 distinct compositions with numerous microstates are designed. A deep neural network, trained on 453 computed datasets, predicts hydrogen adsorption energy (∆ E H* ) across 20 000 microstructures per composition, enabling statistical mapping of site‐specific performance. To capture the effect of local atomic environments, a novel statistical evaluation approach is introduced that quantifies the number of microstates falling within the optimal ∆ E H* range, advancing beyond conventional mean‐based evaluations. Among all candidates, Pt 3 (CrMnFeCo) emerges as the most promising HER catalyst, validated experimentally over a wide pH range. Further in‐depth data mining reveals that surface Co, Cr, and Fe optimize Pt‐Pt‐M sites, while subsurface Ni and Co modulate Pt‐Pt‐Pt interactions. This study establishes a new paradigm for HEIC catalyst design and deepens the mechanistic understanding of activity origin in complex multimetal systems.