Computational Insights Into g-C3N4-Based Heterojunctions for Photocatalytic Water Splitting Reaction.

The growing demand for sustainable hydrogen production has intensified research into photocatalytic water splitting driven by solar energy. Graphitic carbon nitride (g-C3N4), a metal-free polymeric semiconductor with visible-light activity, tunable electronic structure, low cost, and environmental compatibility, has emerged as a promising photocatalyst. Nevertheless, pristine g-C3N4 is limited by insufficient visible-light absorption, sluggish charge transport, rapid electron-hole recombination, and low surface reactivity. Heterojunction engineering has therefore become a key strategy to overcome these intrinsic limitations and enhance photocatalytic efficiency. Distinct from conventional experimental or performance-oriented reviews, this work presents a computationally driven and methodology-oriented review that explicitly addresses how to model g-C3N4-based heterojunction photocatalysts using density functional theory (DFT). Rather than merely summarizing reported efficiencies, this review provides a step-by-step conceptual and practical framework that enables readers to rationally construct, analyze, and optimize heterojunction photocatalysts at the atomic scale. The review begins with the structural and electronic fundamentals of g-C3N4, followed by a mechanistic overview of photocatalytic water splitting, including light absorption, charge generation, interfacial charge separation, and surface redox reactions. Various heterojunction architectures: Type I, Type II, Z-scheme, S-scheme, p-n junctions, Schottky interfaces, and multicomponent systems are systematically discussed, with particular emphasis on interfacial charge-transfer mechanisms revealed by first-principles calculations. A key contribution of this review is the consolidation of DFT-based modeling protocols and descriptors essential for evaluating and enhancing photocatalytic performance. These include structural stability (lattice mismatch, phonon dispersion, and ab initio molecular dynamics), electronic properties (band structure, density of states, and band-edge alignment), charge-transfer characteristics (work function, charge density difference, planar-averaged charge density, and Bader charge analysis), optical properties (absorption spectra, dielectric function, optical band gap, and solar-to-hydrogen efficiency), and carrier transport descriptors (effective mass and carrier mobility). In addition, DFT-based reaction pathway analysis is discussed to elucidate hydrogen and oxygen evolution mechanisms at heterojunction interfaces. Importantly, this review highlights computational strategies to enhance photocatalytic activity, including strain engineering, external electric fields, defect and dopant engineering, and interface optimization, providing clear guidance on how these approaches can be implemented and interpreted within a DFT framework. Challenges related to computational cost, finite-size effects, and realistic interface construction are critically evaluated, along with practical mitigation strategies. Emerging directions such as beyond-DFT methods (GW and TDDFT), machine learning, and high-throughput screening are also discussed as powerful tools for accelerating heterojunction discovery. By integrating model construction principles, computational descriptors, and activity-enhancement strategies into a unified roadmap, this review serves as a practical guide for researchers seeking to design, model, and optimize g-C3N4-based heterojunction photocatalysts using DFT, thereby bridging the gap between theoretical modeling and experimental realization of efficient, stable, and scalable systems for solar-driven hydrogen production.