Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening

• Published in: The journal of physical chemistry letters Vol. 6; no. 18; pp. 3528 - 3533
• Main Authors: Ma, Xianfeng, Li, Zheng, Achenie, Luke E. K, Xin, Hongliang
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 17.09.2015
• Subjects: artificial neural networks; alloys; reactivity descriptors; machine learning; carbon dioxide reduction; density functional theory
• ISSN: 1948-7185; 1948-7185
• DOI: 10.1021/acs.jpclett.5b01660

We present a machine-learning-augmented chemisorption model that enables fast and accurate prediction of the surface reactivity of metal alloys within a broad chemical space. Specifically, we show that artificial neural networks, a family of biologically inspired learning algorithms, trained with a set of ab initio adsorption energies and electronic fingerprints of idealized bimetallic surfaces, can capture complex, nonlinear interactions of adsorbates (e.g., *CO) on multimetallics with ∼0.1 eV error, outperforming the two-level interaction model in prediction. By leveraging scaling relations between adsorption energies of similar adsorbates, we illustrate that this integrated approach greatly facilitates high-throughput catalyst screening and, as a specific case, suggests promising {100}-terminated multimetallic alloys with improved efficiency and selectivity for CO2 electrochemical reduction to C2 species. Statistical analysis of the network response to perturbations of input features underpins our fundamental understanding of chemical bonding on metal surfaces.