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

被引：42

作者：

Fang, Zhaolin ^{[1
]}

Li, Shuyuan ^{[1
]}

Zhang, Yunjiang ^{[1
]}

Wang, Yaxin ^{[1
]}

Meng, Kong ^{[1
]}

Huang, Chenyu ^{[1
]}

Sun, Shaorui ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Environm & Life, Beijing Key Lab Green Catalysis & Separat, Beijing 100124, Peoples R China

来源：

JOURNAL OF PHYSICAL CHEMISTRY LETTERS | 2024年 / 15卷 / 01期

关键词：

TOTAL-ENERGY CALCULATIONS; OXYGEN REDUCTION; ELECTROCATALYSTS; WATER; APPROXIMATION; CARBON; TRENDS;

D O I：

10.1021/acs.jpclett.3c02938

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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.

引用

页码：281 / 289

页数：9

共 54 条

[1] PREDICTED STABILITIES OF MONATOMIC ANIONS IN WATER AND LIQUID-AMMONIA AT 298.15 K [J].