Decoupling Analysis of O₂ Adsorption on Metal-N-C Single-Atom Catalysts via Data-Driven Descriptors

The adsorption energy of adsorbed molecules on single-atom catalysts is a key indicator of the catalytic activity of the catalysts. Developing a generic and interpretable structure-property prediction model from numerous influencing factors is a challenging task. In this work, we constructed a machine learning (ML) model from first-principles calculations of the adsorption energy data of O₂ on Ni(II), Co(II), Cu(II), Fe(II), Fe(III), and Mn(II) single-atom catalysts supported on 15 different N-C substrates under various spin states. A mathematic formula is proposed to predict the adsorption energy by a novel data-driven descriptor derived from physically meaningful factors such as geometric distances and atomic charges. This data-driven descriptor is relevant to only the geometrical configuration of the adsorbate, while the parameters in the linear formulas contain only substrate-specific information. This ML model with the ability to decouple variables will greatly advance the understanding of metal-N-C single-atom catalysts and help in the design of new substrates to modulate catalytic activity.