Accelerating the Discovery of g-C\(_3\)N\(_4\)-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy

Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C\(_3\)N\(_4\) are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on formation energy, Gibbs free energy and bandgap analysis demonstrate that the single atoms of B, Mn and Co anchored on g-C\(_3\)N\(_4\) can serve as highly efficient active sites for hydrogen production. The ML model based on support vector regression (SVR) exhibits the best performance to accurately and rapidly predict the Gibbs free energy of hydrogen adsorption (\({\Delta}\)GH ) for the test set with a lower mean absolute error (MAE) and a high coefficient of determination (R\(^2\)) of 0.45 and 0.81, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valance electron as key descriptors. Overall, the multistep work-flow employed through DFT calculations combined with ML models for efficient screening of potential hydrogen evolution reaction (HER) from g-C\(_3\)N\(_4\)-based single atom catalysis can significantly contribute to the catalyst design and fabrication.