First-principle-data-integrated machine-learning approach for high-throughput searching of ternary electrocatalyst toward oxygen reduction reaction

Platinum (Pt) alloys are expected to overcome long-standing issues of Pt/C electrocatalysts for oxygen reduction reaction (ORR). Entangled with serious uncertainty in configurational and compositional information, the design of a promising multi- component electrocatalyst, however, has been delayed. Here, we demonstrate that a first-principle database-driven machine-learning approach is extremely useful for the purpose via exploring materials beyond the regime of pure quantum mechanical calculations. Guided by a computational ternary phase diagram we indeed experimentally synthesized a PtFeCu nanocatalyst with 2 g per batch capacity and measured its catalytic performance for ORR. Both our computation and experiment consistently demonstrate that PtFeCu is highly active due to the atomic distribution of Cu leading to beneficial modulation of surface strain and segregation. Strikingly, PtFehighCulow (776 mA cm(-2) Pt and 0.67 A mg(-1)Pt) exhibits not only 3-fold better specific andmass activities than Pt/C but also little performance degradation over the accelerated stress test.

Automated summary

This research explores materials beyond the scope of pure quantum mechanical calculations using a machine-learning approach and successfully synthesizes a high-performance PtFeCu nanocatalyst. Both computational and experimental results consistently show that PtFeCu is highly active due to the beneficial modulation of surface strain and segregation caused by the atomic distribution of Cu.