Accelerating the Discovery of g-C3N4-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy

Two-dimensional materials supported by single atom catalysis (SAC) is 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 nonmetal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of graphitic carbon nitride (g-C3N4) are screened by density functional theory (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-C3N4 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 (ΔGH) with a lower mean absolute error (MAE) and a high coefficient of determination (R2) of 0.08 eV and 0.95, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valence electron as key descriptors. Overall, the multistep workflow employed through DFT calculations combined with ML models for efficient screening of potential candidates for hydrogen evolution reaction (HER) from g-C3N4-based single atom catalysis can significantly contribute to the catalyst design and fabrication.