(426d) Discovery of Pt Trimetallic Electrocatalysts for Ammonia Oxidation with Interpretable Deep Learning
AIChE Annual Meeting
2022
2022 Annual Meeting
Catalysis and Reaction Engineering Division
Data Science & Machine Learning Approaches to Catalysis I: Interpretable and Theory-Guided Machine Learning For Catalysis Design and Understanding
Wednesday, November 16, 2022 - 9:12am to 9:30am
We first present a brief analysis of the thermodynamics and kinetics for the NH3 oxidation to N2 on various transition metals from density functional theory (DFT) calculations. It is shown that due to the balanced *N bridge and hollow binding energies only Pt and Ir are able to oxidize NH3 to N2. Subsequently, linear energy-scaling relationships were integrated with a microkinetic model to develop an activity volcano map that can explain activity trends on metals and state-of-the-art Pt-based alloys. To screen for active and stable trimetallic electrocatalysts, we use the activity map and TinNet within an active learning scheme with different filters, e.g., reactivity, synthesizability, and stability metrics. From a vast design space consisting of Pt-based trimetallic alloys we find promising stable and active candidates, e.g. Pt3Ru doped with Co. This approach is verified via controlled synthesis and electrochemical testing of the catalyst. Lastly, SHapley Additive exPlanations (SHAP) analysis is used to gain fundamental understanding of physical factors that govern the site reactivity and to identify key design rules to further aid catalyst discovery.