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

TL;DR

This study combines density functional theory and machine learning to efficiently identify promising single atom catalysts supported on g-C3N4 for hydrogen evolution, reducing computational costs and accelerating catalyst discovery.

Contribution

It introduces a combined DFT and ML workflow for rapid screening of single atom catalysts on g-C3N4, highlighting key descriptors and demonstrating high prediction accuracy.

Findings

B, Mn, and Co single atoms are highly effective for HER.

Support vector regression outperforms other ML models in predicting Gibbs free energy.

Key features include formation energy, bond length, and electronic properties.

Abstract

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.