A Generalizable and Interpretable Framework for Open‐Circuit Voltage Prediction in Organic Solar Cells: Enabling Efficient Screening for High‐Performance Photovoltaics

The open‐circuit voltage ( V oc ) is a key determinant of the power conversion efficiency in organic solar cells (OSCs), a promising technology for affordable and scalable renewable energy. However, existing computational methods for V oc prediction often demand substantial resources, limiting their practicality for large‐scale material screening and device optimization. To address this challenge, this study proposes two highly efficient, data‐driven linear models for accurate V oc estimation. The first model, tailored for fixed donor systems (e.g., PM6, PBDB‐T), employs only the acceptor’s LUMO energy or adiabatic electron affinity, achieving a relative mean error of 2.93%–5.90% ( R 2 = 0.89–0.91) with merely 20 training samples. Rigorous repeated random‐sampling validation across 5000 iterations confirms the remarkable stability and robustness of this minimalist strategy. The second model extends applicability across diverse donors by incorporating the donor’s positive electrostatic potential, forming a universal prediction framework with an average relative error of 3.33%. These models significantly reduce computational cost and data dependency, enabling rapid screening of materials. By facilitating the development of high‐performance organic photovoltaics, this work contributes to the sustainable transition of energy systems and supports the scalable integration of solar energy technologies into future smart grids and urban energy networks.