Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks.

ABSTRACT: The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt₃Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and exp

SUBMITTER: Pillai HS 

PROVIDER: S-EPMC9922329 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature