Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N2 and CO2 Reduction Reactions

The applications of machine learning (ML) in complex interfacial interactions are hindered by the time-consuming process of manual feature selection and model construction. An automated ML program was implemented with four subsequent steps: data distribution analysis, dimensionality reduction and clustering, feature selection, and model optimization. Without the need of manual intervention, the descriptors of metal charge variance (ΔQCT) and electronegativity of substrate (χsub) and metal (δχM) were raised up with good performance in predicting electrochemical reaction energies for both nitrogen reduction reaction (NRR) and CO2 reduction reaction (CO2RR) on metal-zeolites and MoS2 surfaces. The important role of interfacial interactions in tuning the catalytic reactivity in NRR and CO2RR was highlighted from SHAP analysis. It was proposed that Fe-, Cr-, Zn-, Nb-, and Ta-zeolites are favorable catalysts for NRR, while Ni-zeolite showed a preference for CO2RR. An elongated bond of N2 or a bent configuration of CO2 was shown in V-, Co-, and Mo-zeolites, indicating that the molecule could be activated after the adsorption in both NRR and CO2RR pathways. The generalizability of the automatically built ML model is demonstrated from applications to other catalytic systems such as metal-organic frameworks and SiO2 surfaces. The automated ML program is a useful tool to accelerate the data-driven exploration of relationship between structures and material properties without the need of manual feature selection.