Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys

Abstract

Summary Developing physically transparent and quantitatively accurate models that relate the chemical interaction (chemisorption strength) between an adsorbate and a solid surface to the adsorption site's geometry is critical for our understanding of catalysis, corrosion, and electrochemistry. We develop a theory-guided machine-learning (ML) approach, which uses an interpretable class of ML models called generalized additive models (iGAM models), to discover predictive structure-property models that can quantify and explain the link between the geometric structure of an adsorption site and the chemisorption strength. We demonstrate the approach through several case studies, where we analyze the chemisorption strength of chemically distinct adsorbates (O, OH, S, and Cl) on subsurface metal alloy surfaces subjected to various strain- and ligand-induced changes in the local geometric structure. By comparing the ML-derived chemisorption models to previously established electronic-structure models, we clarify the critical physical parameters that control the chemisorption process on metal surfaces.