Machine-learning-accelerated screening of single metal atoms anchored on MnPS3 monolayers as promising bifunctional oxygen electrocatalysts

Xinyi Li; Shiru Lin; Tingyu Yan; Zhongxu Wang; Qinghai Cai; Jingxiang Zhao

doi:10.1039/d3nr02130k

Machine-learning-accelerated screening of single metal atoms anchored on MnPS₃ monolayers as promising bifunctional oxygen electrocatalysts

Nanoscale. 2023 Jul 13;15(27):11616-11624. doi: 10.1039/d3nr02130k.

Authors

Xinyi Li^{1

2}, Shiru Lin³, Tingyu Yan¹, Zhongxu Wang¹, Qinghai Cai^{1

4}, Jingxiang Zhao¹

Affiliations

¹ College of Chemistry and Chemical Engineering, and Key Laboratory of Photonic and Electronic Bandgap Materials, Ministry of Education, Harbin Normal University, Harbin, 150025, China. zhaojingxiang@hrbnu.edu.cn.
² State Key Laboratory of Automotive Simulation and Control, School of Materials Science and Engineering, Key Laboratory of Automobile Materials of MOE, Jilin University, Changchun 130012, China.
³ Division of Chemistry and Biochemistry, Texas Woman's University, Denton, Texas 76204, USA. slin6@twu.edu.
⁴ Heilongjiang Province Collaborative Innovation Center of Cold Region Ecological Safety, Harbin 150025, China.

PMID: 37377102
DOI: 10.1039/d3nr02130k

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 (d_TM-O), the number of d electrons (N_e), the d-center (ε_d), the radius (r_TM) and the first ionization energy (I_m) 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.