Machine Learning‐Guided Design of L1₂‐Type Pt‐Based High‐Entropy Intermetallic Compound for Electrocatalytic Hydrogen Evolution

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.