Machine-learning-accelerated screening of single metal atoms anchored on MNPS3 monolayer as promising bifunctional oxygen electrocatalysts

Date
2023
Authors
Li, Xinyi
Lin, Shiru
Yan, Tingyu
Wang, Zhongxu
Cai, Qinghai
Zhao, Jingxiang
Journal Title
Journal ISSN
Volume Title
Publisher
Royal Society of Chemistry
Abstract

Searching for bifunctional oxygen electrocatalysts with good catalytic performance to promote the oxygen evolution/reduction reactions (OER/ORR) is of great significance to the development of sustainable and renewable clean energy. Herein, we performed density functional theory (DFT) and machine-learning (DFT–ML) hybrid computations to investigate the potential of a series of single transition metal atoms anchored on the experimentally available MnPS3 monolayer (TM/MnPS3) as the bifunctional electrocatalysts for the ORR/OER. The results revealed that the interactions of these metal atoms with MnPS3 are rather strong, thus guaranteeing their high stability for practical applications. Remarkably, the highly efficient ORR/OER can be achieved on Rh/MnPS3 and Ni/MnPS3 with lower overpotentials than those of metal benchmarks, which can be further rationalized by establishing the volcano and contour plots. Furthermore, the ML results showed that the bond length of TM atoms with the adsorbed O species (dTM–O), the number of d electrons (Ne), the d-center (εd), the radius (rTM) and the first ionization energy (Im) of the TM atoms are the primary descriptors featuring the adsorption behavior. Our findings not only suggest novel highly efficient bifunctional oxygen electrocatalysts, but also provide cost-effective opportunities for the design of single-atom catalysts using the DFT–ML hybrid method.

Description
Article originally published in Nanoscale, 15, 11616-11624. English. Published online 2023. https://doi.org/10.1039/d3nr02130k
These files are currently under a publisher-required embargo that will be lifted on June 1, 2024
Keywords
Single-atom catalysts, MnPS3 substrate, Oxygen electrocatalysts, Density functional theory, Machine learning
Citation
This is a post-print version of an article that is available at: https://doi.org/10.1039/d3nr02130k. Recommended citation:Li, X., Lin, S., Yan, T., Wang, Z., Cai, Q., & Zhao, J.-X. (2023). Machine-learning-accelerated screening of single metal atoms anchored on MNPS3 monolayer as promising bifunctional oxygen electrocatalysts. Nanoscale, 15, 11616-11624. This item has been deposited in accordance with publisher copyright and licensing terms and with the author’s permission.