Rising global energy demand, accompanied by environmental
concerns
linked to conventional fossil fuels, necessitates a shift toward cleaner
and sustainable alternatives. This study focuses on the machine-learning
(ML)-driven high-throughput screening of transition-metal (TM) atom
intercalated g-C3N4/MX2 (M = Mo,
W; X = S, Se, Te) heterostructures to unravel the rich landscape of
possibilities for enhancing the hydrogen evolution reaction (HER)
activity. The stability of the heterostructures and the intercalation
within the substrates are verified through adhesion and binding energies,
showcasing the significant impact of chalcogenide selection on the
interaction properties. Based on hydrogen adsorption Gibbs free energy
(ΔG
H) computed via density functional
theory (DFT) calculations, several ML models were evaluated, particularly
random forest regression (RFR) emerges as a robust tool in predicting
HER activity with a low mean absolute error (MAE) of 0.118 eV, thereby
paving the way for accelerated catalyst screening. The Shapley Additive
exPlanation (SHAP) analysis elucidates pivotal descriptors that influence
the HER activity, including hydrogen adsorption on the C site (HC), MX layer (HMX), S site (HS), and
intercalation of TM atoms at the N site (IN). Overall,
our integrated approach utilizing DFT and ML effectively identifies
hydrogen adsorption on the N site (site-3) of g-C3N4 as a pivotal active site, showcasing exceptional HER activity
in heterostructures intercalated with Sc and Ti, underscoring their
potential for advancing catalytic performance.