The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities

Zhaolin Fang; Shuyuan Li; Yunjiang Zhang; Yaxin Wang; Kong Meng; Chenyu Huang; Shaorui Sun

doi:10.1021/acs.jpclett.3c02938

The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities

J Phys Chem Lett. 2024 Jan 11;15(1):281-289. doi: 10.1021/acs.jpclett.3c02938. Epub 2024 Jan 2.

Authors

Zhaolin Fang¹, Shuyuan Li¹, Yunjiang Zhang¹, Yaxin Wang¹, Kong Meng¹, Chenyu Huang¹, Shaorui Sun¹

Affiliation

¹ Beijing Key Laboratory for Green Catalysis and Separation, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.

PMID: 38166444
DOI: 10.1021/acs.jpclett.3c02938

Abstract

The oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) are crucial for the conversion of clean energy. Recently, dual-metal-site catalysts (DMSCs) have gained much attention due to their high atom utilization, stronger stability, and better catalytic performance. An advanced method that combines density functional theory (DFT) and machine learning (ML) has been employed in this study to investigate the adsorption free energies of adsorbates on hundreds of potential catalysts, with the aim of screening for catalysts that are highly active for the ORR and OER. The result of this study is that 30 DMSCs with ORR activity superior to Pt, 10 DMSCs with OER activity superior to RuO₂, and 4 bifunctional catalysts for the OER and ORR are identified. This work provides guidance for the rational selection of metals on DMSCs to prepare catalysts with a high electrocatalytic performance for renewable energy applications.