Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts.

Electrocatalytic water splitting is an attractive way to generate hydrogen and oxygen for obtaining clean energy. Oxygen evolution reaction (OER), as one of the half reactions of oxygen evolution, is kinetically unfavorable involving the transfer of four electrons. Hydroxides are promising candidates for efficient OER electrocatalysts toward water splitting because of their high intrinsic activity and active surface area. However, quantitative prediction of hydroxide electrocatalytic performances from high-dimensional component spaces remains a challenge, severely hindering the performance-oriented precise composition and process design. Herein, we introduce a machine learning-based OER activity prediction method for hydroxide catalysts under extensive doping space for the first time. The relationship among composition, morphology, phase, pH value of the electrolyte, type of the working electrode, and overpotential was successfully fitted by the random forest algorithm. The model shows a good precision on the forecast of new experiments with a mean relative error of 4.74%. Furthermore, a new high-activity hydroxide catalyst NiFeLa was rationally designed and experimentally prepared, showing an ultra-low OP of 226 mV for a current density of 10 mA cm. This work provides an effective and novel way for hydroxide electrocatalyst prediction, which can further enhance the electrocatalyst design toward high catalytic performance.