The greatest obstacle to the wide application of fuel cells is the high cost of its cathode material. The concept of single-atom catalysts offers a new path toward low-cost and highly active cathode materials. Systematic design of single-atom catalysts requires a descriptor that links the intrinsic properties of both metal centers and ligands to the activity of cathodic reactions of the fuel cells, especially the oxygen reduction reaction (ORR). Currently, most of the proposed descriptors aim at the activity of different metal atoms loaded on a specific class of ligands, while the effects of the ligand composition are less emphasized. To propose a universal descriptor that considers both metal centers and ligands, we investigated the most determining intrinsic factors of single-atom catalysts on the reaction free energy (ΔGr) of key elementary steps of ORR. Our investigation is carried on by going through those ΔGr reported in literature and examining the importance percentage of the factors with the gradient boosting regression method, a machine learning technique. OOH protonation was taken as an example. The ligands considered in this work are two-dimensional layered polyaniline CxNy, pyridine and pyrrole, and the metal centers involved are Fe, Ni and Pt. Our results show that the factor most strongly correlated with ΔGr of OOH protonation is the number of valence electrons at the metal center, of which the importance percentage is 62%. Also, the electronegativity of the nearest-neighbor atoms is found to be important in determining ORR activity. Apart from these factors that are generally taken into account, we also find that the electronegativity of the second nearest-neighbor atoms shares almost identical importance with respect to that of the nearest-neighbor atoms, around 15%. Such a finding reveals the significance of considering the second nearest-neighbor atoms when developing descriptors. A universal descriptor for single-atom catalysts with different metal centers and different ligands is under development. The discovery of the effects of ligands can also serve as the foundation of a universal descriptor for more complex catalysts and thus provides a useful tool for screening a wide range of candidates for ORR catalysts.

Revealing effects of ligands on activity of single-atom catalysts: A machine learning study

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