Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning. Issue 11 (5th March 2020) - Lin, Shiru; Xu, Haoxiang; Wang, Yekun; Zeng, Xiao Cheng; Chen, Zhongfang
Abstract : The oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal–air batteries. Abstract : The oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal–air batteries. Graphene-supported single-atom catalysts (SACs) have been widely explored; however, either experiments or density functional theory (DFT) computations cannot screen catalysts at high speed. Herein, based on DFT computations of 104 graphene-supported SACs (M@C3, M@C4, M@pyridine-N4, and M@pyrrole-N4 ), we built machine learning (ML) models to describe the underlying pattern of easily obtainable physical properties and limiting potentials (mean square errors = 0.027/0.021/0.035 V for the ORR/OER/HER, respectively) and employed these models to predict the catalytic performance of 260 other graphene-supported SACs containing metal-N x C y active sites (M@N x C y ). We recomputed the top catalysts recommended by ML towards the ORR/OER/HER by DFT, which confirmed the reliability of our ML model, and identified two OER catalysts (Ir@pyridine-N3 C1 and Ir@pyridine-N2 C2 ) outperforming noble metal oxides, RuO2 and IrO2 . The ML models quantitatively unveiled the significance of various descriptors and quickly narrowed down the candidate list of graphene-supported single-atom catalysts. This approach can be easily used to screen and design other SACs and significantly accelerate the catalyst design for many other important reactions.