Study of the adsorption sites of high entropy alloys for CO2 reduction using graph convolutional network

Abstract

Carbon dioxide reduction is a major step toward building a cleaner and safer environment. There is a surge of interest in exploring high-entropy alloys (HEAs) as active catalysts for CO2 reduction; however, so far, it is mainly limited to quinary HEAs. Inspired by the successful synthesis of octonary and denary HEAs, herein, the CO2 reduction reaction (CO2RR) performance of an HEA composed of Ag, Au, Cu, Pd, Pt, Co, Ga, Ni, and Zn is studied by developing a high-fidelity graph neural network (GNN) framework. Within this framework, the adsorption site geometry and physics are employed through the featurization of elements. Particularly, featurization is performed using various intrinsic properties, such as electronegativity and atomic radius, to enable not only the supervised learning of CO2RR performance descriptors, namely, CO and H adsorption energies, but also the learning of adsorption physics and generalization to unseen metals and alloys. The developed model evaluates the adsorption strength of ∼3.5 and ∼0.4 billion possible sites for CO and H, respectively. Despite the enormous space of the AgAuCuPdPtCoGaNiZn alloy and the rather small size of the training data, the GNN framework demonstrated high accuracy and good robustness. This study paves the way for the rapid screening and intelligent synthesis of CO2RR-active and selective HEAs.