Issue 28, 2023

Accelerating high-throughput screening of hydrogen peroxide production via DFT and machine learning

Abstract

On-site production of hydrogen peroxide (H2O2) using electrochemical methods received considerable attention but was limited by the target product selectivity. In this work, an efficient approach was proposed to accelerate high-throughput screening of H2O2 by utilizing ΔG*O and ΔG*OOH as descriptors in a contour map. Using this method, 21 systems with high selectivity and activity for H2O2 production were screened from 150 different (nitrogen-doped) graphene-supported metal single atom catalysts. The constant-potential and kinetic analysis demonstrated that PdN4, PtN4, NiN4, and CuN4 all exhibited selectivity for the 2e ORR under both acidic and basic conditions, consistent with current experimental research findings. A compressed sensing data analytics approach further unveils the inherent correlation between the structure and catalytic performance, accurately predicting the descriptors, thereby significantly reducing the time and cost. This study enriches the understanding of ORR activity origins and advances rational SAC design for various catalytic reactions, leveraging a combination of DFT and machine learning.

Graphical abstract: Accelerating high-throughput screening of hydrogen peroxide production via DFT and machine learning

Supplementary files

Article information

Article type
Paper
Submitted
29 Mar 2023
Accepted
28 May 2023
First published
29 May 2023

J. Mater. Chem. A, 2023,11, 15426-15436

Accelerating high-throughput screening of hydrogen peroxide production via DFT and machine learning

W. Li, G. Feng, S. Wang, X. Zhong, Z. Yao, S. Deng and J. Wang, J. Mater. Chem. A, 2023, 11, 15426 DOI: 10.1039/D3TA01859H

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