Symbolic regression assisted design of perovskite-type OER electrocatalysts for oxygen evolution reaction (OER) in alkaline electrolyte

Perovskite oxides are promising electrocatalysts for oxygen evolution reaction (OER) in alkaline electrolytes. With high activity and low cost, they are being pursued by researchers worldwide. However, due to the compositional and structural diversity of perovskite-type oxides, the conventional method based on experimental trials on thousands of elemental combinations is inefficient and time-consuming, which hampers the development of this type of electrocatalyst. Here we employed a symbolic regression (SR) method under a machine learning framework to build mathematical formulae that best fit datasets based on 105 experimentally studied perovskite-type electrocatalysts and identified a simple parameter as the OER activity descriptor. Results show that B-site cations with a large number of d electrons and a small radius, such as Ni4+, Co4+, and Fe4+, are beneficial to high OER activity in perovskite oxides, which provide a guide for materials design.