Unveiling the impact of axial ligands on Fe-N-C complexes through DFT simulation and machine learning analysis

Abstract

Single-atom catalysts (SACs), featuring isolated metal atoms embedded in graphitic carbon materials, have attracted considerable research interest due to their cost-effectiveness, high catalytic activity, and customizable functionality across various catalytic reactions. Among SACs, the Fe-N4-C class has garnered significant attention. Tailoring the properties of Fe-N4 sites through localized chemical modifications stands as a key strategy for catalyst engineering. Recent experimental and computational investigations have underscored the distinct influence of axial ligands on Fe in modulating the oxygen reduction reaction (ORR) activity. However, the precise quantitative structure-property relationship between ligands and the catalytic properties of the Fe center remains elusive. In this study, we combined the density functional theory (DFT) simulations and machine learning (ML) models to unravel the relationship between the ligand properties and the oxo binding energy. This energy pertains to the binding of an oxygen atom to the Fe center, a fundamental step in ORR. Through the design of 33 ligands and 5 molecular complexes that accommodate the Fe-N4 moiety, we screened a total of 278 oxo binding energies across an array of ligands and host complexes. Harnessing the power of ML models, we achieved an accurate prediction of these oxo binding energies using features collected from DFT simulations. Notably, the predominant features contributing to the oxo binding energy prediction primarily derived from complexes with attached ligands, rather than isolated ligand properties. We formulated an approach that leverages these critical features and identified the isolated ligand properties capable of effectively predicting these features. This methodology can potentially be applied to investigate other ORR intermediates and a comprehensive understanding of the ligand effect for the ORR activity in SACs can be achieved.