(488f) Bayesian Chemisorption Model for Adsorbate-Specific Tuning of Electrocatalysis

In recent years, there has been a rapid rise in the development and application of machine learning algorithms in catalysis. The machine-learning models developed for materials properties prediction are often considered as “black box”, thus providing limited physicochemical insights into a particular system. Another area of machine learning in materials research is employing the open-box, Bayesian approach [4], which utilizes available physical models and learns model parameters from data. In this talk, we demonstrate that by marrying the Newns-Anderson model with ab initio data in Bayes’s rule [5], the Bayesian model of chemisorption can be developed for probing orbitalwise nature of adsorbate-surface interactions and (electro)-catalytic processes with uncertainty quantification.

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