Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for the oxygen reduction reaction

Highlights

• The catalytic activity of six types of high-entropy alloys was studied theoretically

• Machine learning shows great potential to tackle the huge chemical space of HEAs

• The well-trained model can accurately predict catalytic performance of HEAs

• A strategy to improve catalytic activity by tuning HEA compositions was proposed

Summary

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 Ir₄₈Pt₇₄Ru₃₀Rh₃₀Ag₇₄. Our work offers a guide to the rational design and nanostructure synthesis of HEA catalysts.