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

Hemanth Somarajan Pillai, Yi Li (), Shih-Han Wang, Noushin Omidvar, Qingmin Mu, Luke E. K. Achenie, Frank Abild-Pedersen, Juan Yang, Gang Wu () and Hongliang Xin ()
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Hemanth Somarajan Pillai: Virginia Polytechnic Institute and State University
Yi Li: Jiangsu University
Shih-Han Wang: Virginia Polytechnic Institute and State University
Noushin Omidvar: Virginia Polytechnic Institute and State University
Qingmin Mu: Virginia Polytechnic Institute and State University
Luke E. K. Achenie: Virginia Polytechnic Institute and State University
Frank Abild-Pedersen: SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory
Juan Yang: Jiangsu University
Gang Wu: University at Buffalo, The State University of New York
Hongliang Xin: Virginia Polytechnic Institute and State University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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 experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. 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.

Date: 2023
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DOI: 10.1038/s41467-023-36322-5

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