Bayesian learning of chemisorption for bridging the complexity of electronic descriptors - Siwen Wang; Hemanth Somarajan Pillai; Hongliang Xin (Nature Communications, 2020)
Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–substrate interactions.