Wan Xuhao, Zhang Zhaofu, Yu Wei, Niu Huan, Wang Xiting, Guo Yuzheng
School of Electrical Engineering, Wuhan University, Wuhan, Hubei 430072, China.
The Institute of Technological Sciences, Wuhan University, Wuhan, Hubei 430072, China.
Patterns (N Y). 2022 Aug 2;3(9):100553. doi: 10.1016/j.patter.2022.100553. eCollection 2022 Sep 9.
High-entropy alloys (HEAs) have recently been applied in the field of heterogeneous catalysis benefiting from vast chemical space. However, huge chemical space also brings extreme challenges for the comprehensive study of HEAs by traditional trial-and-error experiments. Therefore, the machine learning (ML) method is presented to investigate the oxygen reduction reaction (ORR) catalytic activity of millions of reactive sites on HEA surfaces. The well-performed ML model is constructed based on the gradient boosting regression (GBR) algorithm with high accuracy, generalizability, and simplicity. In-depth analysis of the results demonstrates that adsorption energy is a mixture of the individual contributions of coordinated metal atoms near the reactive site. An efficient strategy is proposed to further boost the ORR catalytic activity of promising HEA catalysts by optimizing the HEA surface structure, which recommends a highly efficient HEA catalyst of IrPtRuRhAg. Our work offers a guide to the rational design and nanostructure synthesis of HEA catalysts.
高熵合金（HEAs）近来凭借其广阔的化学空间而被应用于多相催化领域。然而，巨大的化学空间也给通过传统的试错实验对高熵合金进行全面研究带来了极大挑战。因此，提出了机器学习（ML）方法来研究高熵合金表面数百万个反应位点的氧还原反应（ORR）催化活性。基于梯度提升回归（GBR）算法构建了性能良好的ML模型，该模型具有高精度、通用性和简单性。对结果的深入分析表明，吸附能是反应位点附近配位金属原子各自贡献的混合。通过优化高熵合金表面结构，提出了一种进一步提高有前景的高熵合金催化剂ORR催化活性的有效策略，该策略推荐了一种高效的IrPtRuRhAg高熵合金催化剂。我们的工作为高熵合金催化剂的合理设计和纳米结构合成提供了指导。
