Application of QSPR Modeling in Designing and Prediction of Power Conversion-Efficient Solar Cell

The advancement of technology and industrialization demands clean, economic, and reliable energy sources that can be fulfilled by high-volume and power-efficient production of solar cells. To speed up the solar cell development process in a rational way, in the last decade, molecular modeling and machine learning (ML) have shown enough potential to accomplish this task. Especially, quantitative structure–property relationships (QSPRs) modeling has reported designing of promising lead components for diverse solar cell systems with higher power conversion efficiency (%PCE) than the existing ones. Until now, most of the QSPR models have been employed for dye-sensitized solar cells (DSSCs) and polymer solar cells (PSCs) followed by designing acceptor and donor components of these systems. But this chapter also encourages the future application of QSPR for quantum dot solar devices (QDSC’s), perovskite solar cells to improve their efficiency further. The present chapter deals with the role of QSPR modeling in solar cells and discusses how QSPR can be implemented in solar cell designing as well as the virtual screening of materials databases. Additionally, solar cell databases and preparation of webserver for future prediction of %PCE, along with other photophysical parameters, are meticulously discussed to provide an easy start for the beginners. Successful QSPR models for DSSCs and PSCs are also illustrated with detailed modeling information followed by mechanistic introspection.