Machine learning-aided discovery of low-Pt high entropy intermetallic compounds for electrochemical oxygen reduction reaction

Abstract

Advancing the design of cathode catalysts to significantly maximize platinum utilization and augment the longevity has emerged as a formidable challenge in the field of fuel cells. Herein, we rationally design a high entropy intermetallic compound (HEIC, Pt(FeCoNiCu)3) for catalyzing oxygen reduction reaction (ORR) by an efficient machine learning stategy, where crystal graph convolutional neural networks are employed to expedite the multicomponent design. Based on a dataset generated from first-principles calculations, the model can achieve a high prediction accuracy with mean absolute errors of 0.003 for surface strain and 0.011 eV atom−1 for formation energy. In addition, we identify two chemical features (atomic size difference and mixing enthalpy) as new descriptors to explore advanced ORR catalysts. The carbon supported Pt(FeCoNiCu)3 catalyst with small particle size is successfully synthesized by a freeze-drying-annealing technology, and exhibits ultrahigh mass activity (4.09 A mgPt−1) and specific activity (7.92 mA cm−2). Meanwhile, The catalyst also shows significantly enhanced electrochemical stability which can be ascribed to the sluggish diffussion effect in the HEIC structure. Beyond offering a promising low-Pt electrocatalysts for fuel cell cathode, this work offers a new paradigm to rationally design advanced catalysts for energy storage and conversion devices. Graphical abstract: [Figure not available: see fulltext.]