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Non-fullerene materials have attracted attention as high-performance molecular acceptors in organic solar cells (OSCs). A proper understanding of the energy level alignment between donors and non-fullerene acceptors is crucial for photoactive materials selection in designing high-performance non-fullerene OSCs. However, the quantitative assessment for the proper selection of donors and non-fullerene acceptors is still rarely studied, which is seen as time-consuming and complicated tasks. In this study, the optimized Random Forest model based on the electronic descriptors (e.g., highest occupied molecular orbitals levels, lowest unoccupied molecular orbitals levels, and band gap) provides the high predictive power, reaching the coefficient of determination (R²) of 0.85 and 0.80 for the training set and testing set, respectively. The use of machine learning approach benefits the development of non-fullerene …