Introduction Electrooxidation of ammonia to dinitrogen plays a crucial role within the nitrogen transformation cycle. It has many applications including electrochemical sensing of ammonia, wastewater remediation, and direct ammonia fuel cells. While platinum (Pt) based catalysts, specifically terminated with (100) facets, have shown promising activity. They still suffer from a large overpotential (~0.5 V) and surface deactivation1. Various alloys such as PtIr, PtRh and NiCu have offered an approach to tackle these issues. However due to vast combination of metals, the material space consisting of bimetallic alloys can quickly become overwhelming to navigate through. Additionally finding good electrocatalysts which not only have a lower overpotential but are also resistant to surface poisoning can be very difficult in this material space. Building upon our previous work2, machine learning models are built in order to screen through the bimetallic alloy material space. Our recent work showed that the optimization of both *NHx and *OH adsorption energies is key to find more thermodynamically efficient electrocatalysts. Additionally it was also shown that *N adatoms could be a precursor to surface poisoning. Using both these insights we have developed machine learning models to search for materials with optimal *NHx and *OH adsorption energies. This allows us to suggest some potentially efficient electrocatalysts for ammonia electrooxidation.

Accelerating discovery of high-performance electrocatalysts for ammonia oxidation via machine learning

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.