Data-driven structural descriptor for predicting platinum-based alloys as oxygen reduction electrocatalysts

Owing to increasing global demand for carbon neutral and fossil-free energy systems, extensive research is being conducted on efficient and inexpensive electrocatalysts for catalyzing the kinetically sluggish oxygen reduction reaction (ORR) at the cathode of fuel cells. Platinum (Pt)-based alloys are considered promising candidates for replacing expensive Pt catalysts. However, the current screening process of Pt-based alloys is time-consuming and labor-intensive, and the descriptor for predicting the activity of Pt-based catalysts is generally inaccurate. This study proposed a strategy by combining high-throughput first-principles calculations and machine learning to explore the descriptor used for screening Pt-based alloy catalysts with high Pt utilization and low Pt consumption. Among the 77 prescreened candidates, we identified 5 potential candidates for catalyzing ORR with low overpotential. Furthermore, during the second and third rounds of active learning, more Pt-based alloys ORR candidates are identified based on the relationship between structural features of Pt-based alloys and their activity. In addition, we highlighted the role of structural features in Pt-based alloys and found that the difference between the electronegativity of Pt and heteroatom, the valence electrons number of the heteroatom, and the ratio of heteroatoms around Pt are the main factors that affect the activity of ORR. More importantly, the combination of those structural features can be used as structural descriptor for predicting the activity of Pt-based alloys. We believe the findings of this study will provide new insight for predicting ORR activity and contribute to exploring Pt-based electrocatalysts with high Pt utilization and low Pt consumption experimentally.