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.

1. K. Nørskov, Rep. Prog. Phys. 53, 1253 (1999).
2. M. Newns, Phys. Bl. 32, 611 (1976).
3. Hammer and J. K. Nørskov, Nature 376, 238 (1995).
4. C. Kennedy and A. O’Hagan, J. R. Stat. Soc. Series B Stat.
5. M. Lee, Bayesian Statistics: An Introduction, 4th ed. (Wiley, 2012).