Mechanistically Interpretable Artificial Intelligence for Designing Oxygen Electrocatalysts

Abstract

ABSTRACT Oxygen reduction and evolution reactions (ORR and OER) are key electrochemical processes central to energy conversion and chemical transformation. However, the inherently complex, multi‐physics nature of ORR/OER—together with diverse operating environments—poses significant challenges to the rational design of electrocatalysts based on structure–property relationships. To overcome these challenges, we developed Two‐Stage Material Screening (TSMS), an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts. Demonstrated in protonic solid oxide cells (P‐SOCs), TSMS screened 6,940,032 compositions and identified top‐performing candidates that were experimentally validated, achieving a peak power density of 2.68 W cm −2 in fuel cell mode and a current density of 3.51 A cm −2 at 1.3 V in electrolysis mode, with stable performance maintained over 500 h at 600°C. Our analysis revealed that electron affinity is strongly associated with thermodynamic stability, while d‐p hybridization and densification resistance emerge as the primary descriptors governing electrocatalytic activity. By combining predictive modeling with mechanistic understanding, TSMS establishes a versatile and broadly generalizable paradigm for accelerating materials discovery.