DOI: https://doi.org/10.36347/sjet.2025.v13i07.003

Pages: 454-486

Providing calculations with high precision and on the atom scale of materials’ electronic, optical and structural characteristics, DFT has fundamentally changed the field of computational materials science. This review provides an extensive review of DFT’s development from early local and semi-local functionals (LDA, GGA) through more sophisticated hybrid (HSE06, B3LYP) and meta-GGA (SCAN, MVS) methods. In addition, the review includes state-of-the-art beyond-DFT methods such as TDDFT, the GW approximation, and the Bethe–Salpeter Equation (BSE) necessary for excited states and optoelectronic behavior understanding and predictions. The combination of machine learning techniques and DFT is studied as a promising method of accelerating the discovery of new materials. A major emphasis is placed on modelling perovskite, silicon-based, and thin-film solar cells (CIGS, CZTS), using DFT to manage band gaps. The importance of the theory in increasing charge transport, reducing the rates of recombination and increasing power conversion efficiency are emphasized. In the field of environmental sustainability, DFT is applied to design efficient photocatalysts for wastewater purification and hydrogen production to gain knowledge on the process of pollutants decomposition and the toxicity of emerging compounds. DFT-derived data supports lifecycle assessments to shape eco-design in photovoltaic systems, as well as decisions about photovoltaic recyclability, energy payback, and environmental sustainability. This review brings forth DFT’s key role in pushing for breakthrough development for energy, environment, and nanotechnological sectors. Indeed, based on quantum simulations, as well as on the evidence and data-backed approaches, DFT continues to widen the field of sustainable material science and clean energy design.