DFT‐based Machine Learning for Ensemble Effect of Pd@Au Electrocatalysts on CO2 Reduction Reaction

The ensemble effect due to variation of Pd content in Pd−Au alloys have been widely investigated for several important reactions, including CO2 reduction reaction (CO2RR), however, identifying the stable Pd arrangements on the alloyed surface and picking out the active sites are still challenging. Here we use a density functional theory (DFT) based machine‐learning (ML) approach to efficiently find the low‐energy configurations of Pd−Au(111) surface alloys and the potentially active sites for CO2RR, fully covering the Pd content from 0 to 100 %. The ML model is actively learning process to improve the predicting accuracy for the configuration formation energy and to find the stable Pd−Au(111) alloyed surfaces, respectively. The local surface properties of adsorption sites are classified into two classes by the K‐means clustering approach, which are closely related to the Pd content on Au surface. The classification is reflected in the variation of adsorption energy of CO and H: In the low Pd content range (0–60 %) the adsorption energies over the surface alloys can be tuned significantly, and in the medium Pd content (37‐68 %), the catalytic activity of surface alloys for CO2RR can be increased by increase the Pd content and attributed to the meta‐stable active site over the surface. Thus, the active site‐dependent reaction mechanism is elucidated based on the ensemble effect, which provides new physical insights to understand the surface‐related properties of catalysts. The ensemble effect on the CO2 reduction reaction for the Pd@Au catalyst surface was investigated by using density functional theory and machine‐learning (regression and clustering) methods. The active site‐dependent reaction mechanism was found that would attribute to the ensemble effect of Pd over the Au surface.