(696g) Machine Learning Approach for Screening Alloy Surfaces for Stability in Catalytic Reaction Conditions

significant effort to screen many surfaces for their stability, as this requires intensive quantum

chemical calculations. To more efficiently estimate stability, we provide a general and data-efficient

machine learning (ML) approach to accurately and efficiently predict the surface energies of metal

alloy surfaces. Our ML approach introduces an element-centered fingerprint (ECFP) which was

used as a vector representation for fitting models for predicting surface formation energies. The

ECFP is significantly more accurate than several existing feature sets when applied to dilute alloy

surfaces and is competitive with existing feature sets when applied to bulk alloy surfaces or

gas-phase molecules. Models using the ECFP as input can be quite general, as we created models

with good accuracy over a broad set of bimetallic surfaces including most d-block metals, even

with relatively small datasets. For example, using the ECFP, we developed a kernel ridge regression

ML model which is able to predict the surface energies of alloys of diverse metal combinations with

a mean absolute error of 0.017 eV atom−1. Combining this model with an existing model for

predicting adsorption energies, we estimated segregation trends of 596 single-atom alloys

(SAAs) with and without CO adsorbed on these surfaces. As a simple test of the approach, we

identify specific cases where CO does not induce segregation in these SAAs.