Enhanced Oxygen Reduction of Biatom Catalysts on g-CN with Heteroatom Doped: A Theoretical Investigation by Density Functional Theory and Machine Learning

Abstract Biatom catalysts (BACs) are well-known as promising candidates for the oxygen reduction reaction (ORR). However, the vast design space of the BACs causes a high cost for experimental and computational screening. Herein, by employing density functional theory (DFT) calculations and machine-learning (ML) algorithms, we propose a data-driven high-throughput design approach for the rapid screening of ORR catalysts consisting of 3d, 4d, and 5d transition metal dimers (TM2) inserted in pristine and heteroatom (S, C, B, P)-doped g-CN support with excellent activity. According to the DFT calculations and ML predictions, we conclude that the BACs of Ag2/Pd2-C-I, Pt2-B-I, Pd2-P-I, and Pd2/Ag2-C-II can serve as promising candidates for ORR catalysts. The ML analysis reveals that the intrinsic properties of the metal active center play a dominant role in the catalytic activity of BACs, accounting for 71.6% of the total feature importance, while the coordination environment contributes to a lesser extent of 28.4%. Thus, based on the suitable metal active center, doping C/B/P heteroatoms on the g-CN support is an effective way to further improve the catalytic activity. A bridge from intrinsic properties to catalytic activity has been established in this work, which is conducive to unveiling the origin of the ORR activity and accelerating the screening process for high-activity ORR catalysts.