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Directly Predicting Limiting Potentials from Easily Obtainable Physical Properties of Graphene-Supported Single-Atom Electrocatalysts by Machine Learning

Abstract

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 up machine learning (ML) models to describe the underlying pattern of easily obtainable physical properties and limiting potentials (errors = 0.013/0.005/0.020 V for ORR/OER/HER, respectively), and employed these models to predict the catalysis performance of 260 other graphene-supported SACs containing metal-NxCy active sites (M@NxCy). We recomputed the top catalysts recommended by ML towards ORR/OER/HER by DFT, which confirmed the reliability of our ML model, and identified two OER catalysts (Ir@pyridine-N3C1 and Ir@pyridine-N2C2) outperforming noble metal oxides, RuO2 and IrO2. The ML models quantitatively unveiled the significance of various descriptors and fast narrowed down the potential 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.

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Supplementary files

Article information


Submitted
07 Dec 2019
Accepted
04 Feb 2020
First published
05 Feb 2020

J. Mater. Chem. A, 2020, Accepted Manuscript
Article type
Paper

Directly Predicting Limiting Potentials from Easily Obtainable Physical Properties of Graphene-Supported Single-Atom Electrocatalysts by Machine Learning

S. Lin, H. Xu, Y. Wang, X. C. Zeng and Z. Chen, J. Mater. Chem. A, 2020, Accepted Manuscript , DOI: 10.1039/C9TA13404B

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