Machine-Learning-Augmented Chemisorption Model for Metal Oxides

Perovskites with the ABO₃ type structure have attracted enormous research efforts in recent years due to their high activity for catalyzing oxygen evolution in (photo)-electrochemical systems. The thermodynamically and kinetically unfavorable oxygen evolution reaction (OER) is considered as a bottleneck for many energy conversion processes such as metal-air batteries and photocatalytic water splitting cells¹. We aim to design cost-effective metal oxides for catalyzing the sluggish OER. It is, however, very time-consuming and costly to search for highly optimized metal oxides by high-throughput experiments and/or quantum-chemical calculations due to the vast materials space of composition and geometry.

In this poster, we extend our previous study² on metal catalysis to metal oxides by developing a machine-learning-augmented chemisorption model that captures adsorption properties of active sites on metal oxides. To design effective features that the machine-learning algorithms can use to ‘learn’ and predict properties of metal oxides, we applied a feature engineering process on the catalyst database. We explore electronic based features such as the B-site metal e_g band filling, d-band upper edge, oxidation state, d-orbital splitting, ligand strength, and easily accessible structural based features such as the coordination number, together with the intrinsic properties of metal atoms. Using those features, the machine-learning model optimized with available ab initioadsorption energies on perovskite (001) surfaces can capture adsorption energies of *O, *OH, and *OOH intermediates with the root mean squared errors (RMSE) smaller than the DFT-GGA calculation error of 0.2 eV. Compared with the traditional high-throughput computational and experimental trial-and-error approach, the machine-learning chemisorption models exhibit great potential in accelerating the discovery of high-performance oxide materials for electrocatalysis.

1. Suntivich, J., May, K. J., Gasteiger, H. A., Goodenough, J. B. & Shao-Horn, Y. A Perovskite Oxide Optimized for Oxygen Evolution Catalysis from Molecular Orbital Principles. Science 334,1383–1385 (2011).

2. Ma, X., Li, Z., Achenie, L. E. K. & Xin, H. Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening. J. Phys. Chem. Lett. 6, 3528–3533 (2015).