Machine Learning–Guided Surface Strain Engineering in Connected Platinum–Nickel Nanoparticle Catalysts for Advanced Oxygen Reduction Performance
ABSTRACT Engineering the surface structure of catalysts is critical for achieving high intrinsic activity in the oxygen reduction reaction (ORR). We report a machine‐learning (ML)‐guided materials design strategy for the synthesis of support‐free, connected nanoparticle catalysts with enhanced activity. ML analysis of a dataset comprising 210 Pt‐based ORR catalysts quantitatively evaluated the relative importance of multiple structural, compositional, and electronic descriptors, identifying surface compressive strain (≈−4%) as an effective integrated descriptor strongly associated with ORR specific activity (SA). Guided by this insight, a H2‐annealing‐induced surface structuring approach was developed to construct an interconnected porous Pt–Ni nanoarchitecture with a Pt‐skin (≈3 atomic layers) and tunable compressive strain (≈−3%) over a Ni‐enriched subsurface. The optimized catalyst synthesized under 100% H2‐annealing exhibits exceptional ORR activity, with a SA of 5.1 ± 0.5 mA cmPt − 2, corresponding to a 12‐fold enhancement relative to commercial Pt/C. A linear correlation between surface strain and SA experimentally validates ML predictions and highlights the importance of strain‐engineered surfaces in electrocatalysis. Furthermore, the connected nanonetwork demonstrates remarkable electrochemical durability, retaining substantial compressive strain and exhibiting minimal Ni dissolution after 10,000 potential load cycles. This work establishes a generalizable materials design framework for developing next‐generation high‐activity, durable electrocatalysts for energy conversion technologies.