A General Machine-Learning Framework for High-Throughput Screening for Stable and Efficient RuO2-Based Acidic Oxygen Evolution Reaction Catalysts

Doping guest elements is an effective way to increase the activity and stability of RuO2 catalysts in acidic oxygen evolution reaction (OER). However, due to the vastness of the doping space, it is challenging for either high-cost experiments or density functional theory (DFT) calculations to screen out the doped structures with the optimized catalytic performance. Herein, we reported a machine-learning (ML) framework that aims to realize high-throughput screening for both stability and activity of doped-RuO2 acidic OER catalysts from monodoping to triple-doping at a low level of computational cost. Compared to the d-band theory and some other previous models, our ML model was constructed based on more general input features and realized higher prediction accuracy with mean absolute errors (MAEs) of 0.074, 0.142, and 0.082 eV for *OH, *O, and *OOH adsorption energies, respectively. Through the ML models, three doping structures Ru41Zn7O96, Ru41Fe3Zn4O96, and Ru39Co1Cu4Zn4O96 were found to possess extraordinarily high stability and comparable or higher activity than previously reported OER catalysts. This work provided an efficient study paradigm in fields of material screening and a useful guide for experimental synthesis.