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

SUBMITTER: Niu X 

PROVIDER: S-EPMC12272364 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature