  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.
