Deciphering the structure-activity-selectivity relationship of high-entropy alloys for CO2 reduction via interpretable machine learning.

High-entropy alloys (HEAs) have emerged as a promising class of multisite catalysts that exhibit high levels of performance due to a diversity of active sites; however, establishing their structure-performance relationships remains a grand challenge. Herein, we systematically explored the structure-activity-selectivity relationship of HEAs for the CO2 reduction reaction (CO2RR) with the assistance of a machine learning framework and density functional theory computations. Statistical analysis of hundreds of thousands of binding energies of *CO, *CHO, and *H on (FeCoNiCuMo)55 clusters revealed that HEAs can break the well-established scaling relationship of pure metal catalysts, but they also face an activity-selectivity tradeoff. This originates from the positive role of the unpaired d electron number in enhancing the binding strength of *CHO and *H and limits the overall performance. Moreover, an activity and a selectivity descriptor were constructed, giving accurate predictions for the performance variations of the reported experiments. On this basis, rapid screening among 26 334 types of HEAs was performed, and 10 promising candidates that balanced activity and selectivity were selected. Our workflow not only provides quantitative criteria to accelerate the rational design of HEA catalysts for the CO2RR, but it also offers a systematic approach to unraveling the intricate structure-performance relationship in complex systems.