Performance of the nitrogen reduction reaction on metal bound g-C 6 N 6 : a combined approach of machine learning and DFT.

Developing a cost-effective and environmentally benign substitute for the energy-intensive Haber-Bosch process for the production of ammonia is a global challenge. The electrocatalytic nitrogen reduction reaction (NRR) under ambient conditions through the six proton-electron process has attracted significant interest. Herein, a series of transition-metal (TM) based single atom catalysts (SAC) embedded on carbon nitride (C 6 N 6 ) have been chosen to explore the NRR activity. The promising metals have been primarily screened through density functional theory (DFT) by calculating their adsorption energies on C 6 N 6 - energies for dinitrogen binding and the barriers at the rate determining step. Based on these criteria, amongst the 18 metal centers, Ta based C 6 N 6 emerges as a good candidate for the reduction of nitrogen to NH 3 . On the other hand, for the Machine Learning (ML) regression models, the covalent radius and the d-band center of the TM have been identified as the most correlated descriptors for predicting the adsorption energy of nitrogen on the active metal center. Besides, probabilistic modeling using the soft voting technique in the classification model allows us to predict the most efficient single atom catalyst. Despite the realistic bottleneck of having only a limited number of TMs to choose from, this technique effectively predicts the best catalyst from a modest dataset. With the highest probabilistic score, Ta based C 6 N 6 dominates over the other catalysts in a good agreement with DFT findings. This letter manifests the effectiveness of the soft voting technique in an ensemble-based classification model.