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The complicated trilateral relationships among molecular structures, properties, and photovoltaic performances of electron donor and acceptor materials hinder the rapid improvement of power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, the database of 310 donor and non‐fullerene acceptor pairs is constructed and 39 molecular structure descriptors are selected. Four kinds of machine learning (ML) algorithms random forest (RF), extra trees regression, gradient boosting regression trees, and adaptive boosting are applied to predict photovoltaic parameters. The coefficient of determination, Pearson correlation coefficient, mean absolute error, and root mean square error are adopted to evaluate ML performance. The results show that the RF model exhibits the best prediction accuracy. The Gini important analysis suggests the fused ring and aromatic heterocycles are critical fragments in determining PCE. The molecular unit sets are constructed by cutting each donor and acceptor molecules in database. The 31 752 D‐π‐A‐π type donor molecules and 5 455 164 A‐π‐D‐π‐A type acceptor molecules are designed by recombination of molecular units, and 173 212 367 328 donor–acceptor pairs are generated by combining the newly designed donor and acceptor molecules. Based on the predicted PCE using the trained RF model, 42 donor–acceptor pairs exhibit the predicted PCE > 16%, in which the highest PCE is 16.24%. [ABSTRACT FROM AUTHOR]

The selection of electron donors and nonfullerene acceptors (NFAs) in organic solar cells (OSCs) is crucial for improving photovoltaic performance. Machine learning (ML) has brought a breakthrough solution. Herein, 292 donor‐NFA pairs with experimental OSC parameters from the reported articles are collected. The ML descriptors include device processing parameters, molecular properties, and molecular structure. The five ML regression models, random forest (RF), extra tree regression, gradient boosting regression tree, adaptive boosting, and artificial neural network (ANN) are trained. GridSearchCV is used for hyperparameter optimization of ML regression models. The SHapley Additive exPlanation approach is employed to analyze descriptor importance. Among the trained five ML models, the RF model shows superior performance, achieving Pearson's correlation coefficient (r) of 0.81 on the test set. Based on the donors and NFAs in constructed dataset, the 9779 donor–NFA pairs for OSCs are generated by randomly combining donor and acceptor molecules. The trained RF model is utilized to predict the power conversion efficiency (PCE) of new donor–acceptor pairs for OSCs. The results indicate that the OSC composed of PBDB‐TF as donor and L8‐BO as acceptor can achieve the remarkable PCE of 17.9%. [ABSTRACT FROM AUTHOR]

The modification of fused rings in the central backbone, side chains, and end groups of the non‐fullerene acceptor (NFA) for organic solar cells (OSCs) can modulate properties and photovoltaic performance. In order to investigate the effect of fused rings and side chains on photovoltaic performance, PBDB‐T is selected as electron donor and IDTIC, IDIC, IDIC‐PhC6, IDIC‐C4Ph, 4TIC, ITIC‐OE, IDTTIC, ITC6‐IC, ITIC, and C8‐ITIC are selected as NFAs. Based on quantum chemistry calculations, the geometries, electronic structures, excitation properties, excited‐state lifetimes, and electrostatic potentials (ESPs) of the monomer and the PBDB‐T:NFA complexes are studied, and the rate constants of charge transfer (CT), exciton dissociation (ED) and charge recombination (CR) processes are analyzed. The results show that the increase of fused‐ring in NFA's backbone by substituting phenyl or thienyl with bithiophene can elevate the highest occupied molecular orbital energies, reduce the gap of frontier molecular orbital energies, induce red‐shift of absorption spectrum, increase CT energy, improve CT and ED rates. This work provides a detailed understanding of tuning optoelectronic properties and photovoltaic performance by modifying NFA's side chains and extending backbones. [ABSTRACT FROM AUTHOR]

Molecular stacking modes that determine morphology of bulk‐heterojunctions are important because of their effects on photovoltaic performance of organic solar cells (OSCs). Here, in order to investigate how molecular stacking pattern in OSCs heterojunction impact on electronic structures and properties, and then influence photovoltaic performance, this work selected D18:Y6 OSC as a representative system. On the basis of semi‐empirical quantum chemistry and density functional theory calculations, we studied the geometries, electronic structures and excitation properties, as well as electron and hole couplings of D18 and Y6 molecules, Y6 dimers, D18/Y6 and D18/Y6‐dimer complexes which are heterojunction interface models. The results indicate that the molecular stacking effects on open‐circuit voltage (VOC) can be attributed to the influences on charge transfer (CT) excitation energies because molecular stacking modes at donor/acceptor heterojunction interface can significantly change excitation energies. Furthermore, the molecular stacking modes can cause drastically different electron and hole couplings, and then affect charge transfer/transport rates. Hence, the molecular stacking effects on short‐circuit current density (JSC) can be understood from the effects on electron and hole couplings. The molecular stacking modes that are favorable to improving VOC and JSC of D18:Y6 OSC were also discussed. [ABSTRACT FROM AUTHOR]

• Fluorination of end groups, thiophene modification. • Increasing the number of central rings. • These modifications can decrease the H-L gap. • Fluorine substitution promotes charge transfer at the heterojunction interface. • Increasing the central ring number lowers CR and enhances ED efficiency. Modifying the skeleton, side chains and end groups of non-fullerene acceptors (NFAs) is important approach to improve the photovoltaic performance of organic solar cells. We selected PBDB-T as the donor and INIC, INIC1, INIC2, ITCC, ITCPTC and ITIC as the acceptors in order to study the difference in NFAs. The geometries, electronic structures, excited state properties, excited-state lifetimes and rate constants of charge transfer (CT), charge recombination (CR) and exciton dissociation (ED) processes of the monomers and PBDB-T:NFA complexes have been investigated by means of density functional theory and time-dependent density functional theory. The research reveals that fluorination of end groups, thienyl substitution and increase in the number of central rings result in the decrease in the energy gap between the highest occupied molecular orbital and the lowest unoccupied molecular orbital. Fluorine substitution and increase in central ring numbers cause redshift of the absorption spectrum for NFAs. NFAs with fluorine substitution have a larger maximum electrostatic potential, which facilitates CT at the donor:NFA heterojunction interfaces. Fluorine substitution and thienyl substitution decrease the NFA's CT distances, indicating that both fluorine and thienyl substitutions affect the CT rate. Fluorine substitution, thienyl modification of end groups and increase in central ring numbers also reduce the exciton binding energy, which is beneficial for decreasing CR and improving ED efficiency. [ABSTRACT FROM AUTHOR]