A Review on Fabrication and Quantum Chemical Designing of Organic Solar Cells: Role of DFT and Machine Learning Technologies

Improving the efficiency and performance of organic solar cells (OSCs) requires a close relationship between their structure and characteristics. Photovoltaic solar cells are rapidly advancing due to their high-power conversion efficiencies and low cost. Single-junction polymer solar cells (PSCs) and OSCs offer flexible and wearable device applications, while all-small-molecule organic photovoltaic cells (ASM-OPVs) offer clear molecular structure, ease of purification, and high reproducibility. This study uses machine learning (ML) and Density Functional Theory (DFT) simulations to analyze the electronic structure, energy levels, and charge transport characteristics of organic materials used in OSCs, identifying significant relationships. This review article intends to clarify fundamental design concepts for optimizing OSCs by utilizing the synergy between DFT and ML and controlling the development of new materials and fabrication techniques for efficient and affordable solar devices.