Issue 11, 2020

Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning

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-NxCy active sites (M@NxCy). 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-N3C1 and Ir@pyridine-N2C2) 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.

Graphical abstract: Directly predicting limiting potentials from easily obtainable physical properties of graphene-supported single-atom electrocatalysts by machine learning

Supplementary files

Article information

Article type
Paper
Submitted
07 déc. 2019
Accepted
04 févr. 2020
First published
05 févr. 2020

J. Mater. Chem. A, 2020,8, 5663-5670

Author version available

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, 8, 5663 DOI: 10.1039/C9TA13404B

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements