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

Electrochemical conversion of ammonia to nitrogen has important energy and environmental applications but is hindered by lack of efficient electrocatalysts. Here the authors use quantum chemistry and machine learning to gain insights into the reaction mechanism and accelerate the design of highly active Ir-free trimetallic catalysts. 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, Pt_3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt_3Ir, and Pt_3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d -band center metric of binding sites.