Unraveling disorder-to-order transitions and chemical ordering in PtCoM ternary alloys using machine learning potential

Abstract

PtCo intermetallic alloy nanoparticles are highly active and stable catalysts for the oxygen reduction reaction (ORR), making them key materials for proton-exchange membrane fuel cells. However, the high-temperature annealing required for ordering into the intermetallic phase often leads to particle growth. In this work, we developed a machine learning interatomic potential to model the disorder-to-order transition in PtCo-based ternary alloys with high accuracy and computational efficiency. Monte Carlo simulations reveal that introducing a third element significantly affects both the ordering process and the critical temperature for the disorder-to-order transition. The thermodynamic driving forces for ordering in various PtCoM alloys were systematically investigated to identify potential high-performance PtCoM catalysts. Kinetic analysis further indicates that the accelerated ordering transition in PtCo alloys is primarily driven by lower migration energy barriers and enhanced directional diffusion. These findings provide valuable atomic-scale insights into the chemical ordering mechanisms and suggest a pathway for designing highly ordered PtCo-based nanoparticles for energy conversion and storage applications.