Looking beyond Adsorption Energies to Understand Interactions at Surface using Machine Learning

Abstract

AbstractIdentifying factors that influence interactions at the surface is still an active area of research. In this work, the importance of analyzing bond length activations (BLact) along with adsorption energies (Ea) while interpreting Density Functional Theory (DFT) results is emphasized. Investigating adsorption of different small molecules, such as O2, N2, CO, and CO2, on commonly studied facets ((100), (110), and (111)) of seven fcc transition metal surfaces (M=Ag, Au, Cu, Ir, Rh, Pt, and Pd) demonstrates the missing linear correlation between Ea and BLact. Further, tree based Machine Learning (ML) models reinforce the missing linear correlation between the two parameters and also highlight the importance of analyzing both to develop a better understanding of adsorption at surfaces. The best performing Random Forest models have a mean absolute error (MAE) of 0.19 eV for Ea prediction, and even lower MAE of 0.012 Å for BLact prediction. While often d‐band center is correlated with Ea, our observations show that infact the d‐band center has a better correlation with BLact. These observations emphasizes the role of BLact in gaining a fuller picture for catalysis. The fact that the factors responsible for BLact is a lesser‐explored subject adds to the novelty of the findings.