Predicting Competitive Anion Electrosorption on Late Transition Metals

Investigating competitive anion electrosorption on transition metal surfaces is experimentally challenging but critical for advancing electrocatalytic and electrochemical engineering. Here, we present a rigorous computational framework for predicting anion adsorption as a function of the applied potential by combining grand canonical density functional theory (GC-DFT) with thermodynamic cycles. This approach is calibrated against experimental voltammograms on Pt(111) and applied to a diverse set of anions on late transition metals. Using multiple linear regression with feature importance analysis, we identify physical descriptors governing electrosorption including anion properties such as formal charge and dipole moment, and metal properties such as d-band center and atomic polarizability. We then develop a potential-dependent Langmuir adsorption model to predict competitive anion coverages under realistic electrochemical conditions. Case studies using the Langmuir model demonstrate the impact of electrolyte composition and pH on anion electrosorption trends relevant to electrocatalytic reactions such as nitrate, oxygen, and carbon dioxide reduction. Overall, this study provides a systematic and predictive framework for understanding anion electrosorption phenomena, offering insights for electrode/catalyst and electrolyte design in electrochemistry and electrocatalysis.