Machine-learning-accelerated design of high-performance platinum intermetallic nanoparticle fuel cell catalysts

Abstract

Carbon supported PtCo intermetallic alloys are known to be one of the most promising candidates as low-platinum oxygen reduction reaction electrocatalysts for proton-exchange-membrane fuel cells. Nevertheless, the intrinsic trade-off between particle size and ordering degree of PtCo makes it challenging to simultaneously achieve a high specific activity and a large active surface area. Here, by machine-learning-accelerated screenings from the immense configuration space, we are able to statistically quantify the impact of chemical ordering on thermodynamic stability. We find that introducing of Cu/Ni into PtCo can provide additional stabilization energy by inducing Co-Cu/Ni disorder, thus facilitating the ordering process and achieveing an improved tradeoff between specific activity and active surface area. Guided by the theoretical prediction, the small sized and highly ordered ternary Pt₂CoCu and Pt₂CoNi catalysts are experimentally prepared, showing a large electrochemically active surface area of ~90 m² g_Pt⁻¹ and a high specific activity of ~3.5 mA cm⁻². Platinum-based intermetallic alloys are promising candidates as low-platinum oxygen reduction reaction catalysts for proton exchange membrane fuel cells. Here, the authors develop small sized and highly ordered Pt₂CoCu and Pt₂CoNi catalysts for fuel cells by machine-learning accelerated computational screening.

Keywords

Identifiers

DOI

10.1038/s41467-023-44674-1

Journal

Nature Communications

Year

2024