(463b) Accelerating Discovery of High-Performance Electrocatalysts for Ammonia Oxidation Via Machine Learning
AIChE Annual Meeting
2020
2020 Virtual AIChE Annual Meeting
Sustainable Engineering Forum
Big Data and Analytics for Sustainability
Monday, November 16, 2020 - 8:15am to 8:30am
Our recent work showed that the optimization of *NHx and *OH adsorption energies is key to find 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 potentially efficient electrocatalysts for ammonia electrooxidation3.
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(2) 1. H. S. Pillai, H. Xin, New Insights into Electrochemical Ammonia Oxidation on Pt(100) from First Principles. Ind. Eng. Chem. Res. 58, 10819â10828 (2019).
(3) Y. Li, X. Li, H. S. Pillai, J. Lattimer, N. Mohd Adli, S. Karakalos, M. Chen, L. Guo, H. Xu, J. Yang, D. Su, H. Xin, G. Wu, Ternary PtIrNi Catalysts for Efficient Electrochemical Ammonia Oxidation. ACS Catal. 10, 3945â3957 (2020).