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- W4386569989 abstract "The open-circuit voltage (Voc) in organic solar cells (OSCs) hardly exceeds 1.0 V because of the relatively high voltage loss caused by charge non-radiative recombination at the donor–acceptor interface. Herein, in this paper the machine learning (ML) prediction models are used to explore the relationship among the donor and acceptor structure, electronic properties, and the non-radiative voltage loss (△Vocnon-rad). Among the models, the prediction performance from the optimal random forest (RF) model is improved via 13.48% compared with that of the support vector regression (SVR) model. A combination of correlation and importance is used to collaboratively screen out the key features of acceptor materials with low △Vocnon-rad in OSCs. The importance analysis indicates that the benzene-1,2-diamine, prop-2-en-1-imine and nitrogen sulfur bond are important structure, which represents the electron-deficient unit (A') in the fused-ring core of non-fullerene acceptors (NFAs). It is worth mentioning that the selected key features also have good applicability in the small data with ternary OSCs, and its coefficient of determination (R2) is 0.704 in the testing set. In addition, the four new Y6 derivatives (Y6O, Y6B, Y18B and Y18U) are designed by the screened key features. And the quantum chemical calculations show that the introduction of benzene ring and branched side chain to the A' unit can make the HOMO and LUMO energy levels of the molecule tends to rise-lying. Like, the HOMO-LUMO gap is 2.69 eV and the optical band gap is 1.80 eV in Y18B, which both are smaller than these of Y6. Y18B also has the smallest electrostatic potential of 5.08 kcal/mol on molecular surface. Significantly, it decreases the singlet–triplet energy gap and exciton binding energy of the Y18B for effectively reducing the △Vocnon-rad in the device. This work provides an effective model to significantly accelerate the exploration of new and highly efficient NFAs OSCs with the lower △Vocnon-rad." @default.
- W4386569989 created "2023-09-10" @default.
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- W4386569989 date "2023-11-01" @default.
- W4386569989 modified "2023-10-16" @default.
- W4386569989 title "A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells" @default.
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- W4386569989 doi "https://doi.org/10.1016/j.cej.2023.145958" @default.
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