Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning

Transition metal (such as Fe, Co, and Ni) oxides are excellent systems in the oxygen evolution reaction (OER) for the development of non-noble-metal-based catalysts. However, direct experimental evidence and the physical mechanism of a quantitative relationship between physical factors and oxygen evolution activity are still lacking, which makes it difficult to theoretically and accurately predict the oxygen evolution activity. In this work, a data-driven method for the prediction of overpotential (OP) for (Ni-Fe-Co)O catalysts is proposed via machine learning. The physical features that are more related to the OP for the OER have been constructed and analyzed. The random forest regression model works exceedingly well on OP prediction with a mean relative error of 1.20%. The features based on first ionization energies (FIEs) and outermost d-orbital electron numbers (DEs) are the principal factors and their variances (δ_FIE and δ_DE) exhibit a linearly decreasing correlation with OP, which gives direct guidance for an OP-oriented component design. This method provides novel and promising insights for the prediction of oxygen evolution activity and physical factor analysis in (Ni-Fe-Co)O catalysts.