DOI: https://doi.org/10.36347/sjet.2025.v13i04.007

Pages: 264-287

Photocatalysis, particularly in energy conversion and environmental remediation, has become a significant technology due to its ability to utilise solar energy to degrade pollutants and produce clean energy. At the core of developing effective photocatalysts are quantum chemistry methods, most notably Density Functional Theory (DFT), which can be employed to simulate electronic structures and predict catalytic behaviour. This review paper discusses the theoretical methods used in photocatalysis, focusing on DFT and its developments, including hybrid functionals, meta-GGA, and their range-separated hybrid models. Furthermore, we discuss multi-configurational and perturbation theory methods, which are used for systems with strong electron correlations, and integrating DFT with machine learning to accelerate the discovery of new photocatalytic materials. The paper focuses on DFT's role in synthesising new materials, notably metal-organic frameworks (MOFS). It presents their applications in water-splitting photocatalysis, CO2 reduction, and the degradation of organic pollutants. Finally, we review recent developments in computational methods used to model the mechanisms and reactions of photocatalysis, focusing on the need to optimise the light-matter interface. Despite the immense promise, challenges persist in accurately modelling complex photocatalysis systems, necessitating ongoing advances in computational methods. The advancement of photocatalysis will depend on aligning theory and experiment and refining computational models to optimise the efficiency and scalability of catalysis processes.