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Machine learning (ML) is effective to establish the complicated trilateral relationship among structures, properties, and photovoltaic performance, which is fundamental issue in developing novel materials for improving power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, we constructed the database of 397 donor–acceptor pairs of OSCs with photovoltaic parameters and descriptor sets, which include donor–acceptor weight ratio within the active layer of the OSCs, root mean square of roughness, and 1024-bit Morgan molecular fingerprint for donor (Fp-D) and acceptor (Fp-A). The ML models random forest (RF), adaptive boosting (AdaBoost), extra trees regression, and gradient boosting regression trees were trained based on the descriptor set. The metrics determination coefficient (R2), Pearson correlation coefficient (r), root mean square error, and mean absolute error were selected to evaluate ML model performances. The results showed that the RF model exhibits the highest accuracy and stability for PCE prediction among these four ML models. Moreover, based on the decomposition of non-fullerene acceptors L8-BO, BTP-ec9, AQx-2, and IEICO, 20 acceptor molecules with symmetric A–D–A and A–π–D–π–A architectures were designed. The photovoltaic parameters of the designed acceptors were predicted using the trained RF model, and the virtual screening of designed acceptors was conducted based on the predicted PCE. The results indicate that six designed acceptors can reach the predicted PCE higher than 12% when P3HT was adopted as a donor. While PM6 was applied as a donor, five designed acceptors can achieve the predicted PCE higher than 16%. [ABSTRACT FROM AUTHOR]

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]

Developing non-fullerene acceptors (NFAs) by modifying the backbone, side chains and end groups is the most important strategy to improve the power conversion efficiency of organic solar cells (OSCs). Among numerous developed NFAs, Y6 and its derivatives are famous NFAs in the OSC field due to their good performance. Herein, in order to understand the mechanism of tuning the photovoltaic performance by modifying the Y6's center backbone, π-spacer and side-chains, we selected the PM6:Y6 OSC as a reference and systematically studied PM6:AQx-2, PM6:Y6-T, PM6:Y6-2T, PM6:Y6-O, PM6:Y6-1O and PM6:Y6-2O OSC systems based on extensive quantum chemistry calculations. The results indicate that introducing quinoxaline to substitute thiadiazole in the backbone induces a blue-shift of absorption spectra, reduces the charge transfer (CT) distance (Δd) and average electrostatic potential (ESP), and increases the singlet–triplet energy gap (ΔEST), CT excitation energy and the number of CT states in low-lying excitations. Inserting thienyl and dithiophenyl as π spacers generates a red-shift of absorption spectra, enlarges Δd and average ESP, and reduces ΔEST and the number of CT states. Introducing furo[3,2-b]furan for substituting one thieno[3,2-b]thiophene unit in the Y6's backbone causes a red-shift of absorption spectra and increases ΔEST, Δd and average ESP as well as CT excitation energy. Introducing alkoxyl as a side chain results in a blue-shift of absorption spectra, and increases ΔEST, Δd, average ESP, CT excitation energy and the number of CT states. The rate constants calculated using Marcus theory suggest that all the molecular modifications of Y6 reduce the exciton dissociation and charge recombination rates at the heterojunction interface, while introducing furo[3,2-b]furan and alkoxyl enlarges CT rates. [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]

Molecular descriptors are critical for determining the accuracy of machine learning (ML) study on organic photovoltaics (OPV). To unravel the complex relationship between molecular properties and device performance, on the basis of 510 donor‐acceptor pairs in OPV active layer, the open‐circuit voltage loss (VOC‐loss), dielectric constants of donor and acceptor (ε‐D and ε‐A) were firstly implemented into property descriptor set that includes 41 quantities totally. Then, the five ML algorithms were applied to compare the property descriptor sets with and without VOC‐loss, ε‐D and ε‐A (coded as new and old sets) in the prediction of photovoltaic parameters. The ML results of Pearson's correlation coefficient and the slope of regression lines indicate the performances of new molecular descriptor set are prevailing to that of old set. Furthermore, the Gini important analysis indicates that the ε‐D, ε‐A and VOC‐loss are very important parameters for determining device performance. Higher dielectric constants and lower VOC‐loss will be more beneficial to the performance of OPV devices. [ABSTRACT FROM AUTHOR]

The optoelectronic properties of formamidinium CH(NH2)2+ (FA) doped double perovskite Cs2AgBiBr6 with corresponding formula Cs1.875FA0.125AgBiBr6 were investigated based on electronic structures and supercell models. The dopant formation energies, tolerance and octahedral factors support the doped structures are stable. FA dopant increases band gap about 0.15 eV without introducing dopant states in gap, but also increases the reduced effective masses, exciton binding energies, and elastic constants. However, it decreases exciton energies and charge carrier mobilities. Though the energy barriers among the different FA orientations in Cs1.875FA0.125AgBiBr6 are quite small (<30 meV), FA orientations in doped systems can remarkably impact on electron and hole mobilities. [ABSTRACT FROM AUTHOR]

The optoelectronic properties of formamidinium CH(NH2)2+ (FA) doped double perovskite Cs2AgBiBr6 with corresponding formula Cs1.875FA0.125AgBiBr6 were investigated based on electronic structures and supercell models. The dopant formation energies, tolerance and octahedral factors support the doped structures are stable. FA dopant increases band gap about 0.15 eV without introducing dopant states in gap, but also increases the reduced effective masses, exciton binding energies, and elastic constants. However, it decreases exciton energies and charge carrier mobilities. Though the energy barriers among the different FA orientations in Cs1.875FA0.125AgBiBr6 are quite small (<30 meV), FA orientations in doped systems can remarkably impact on electron and hole mobilities. [ABSTRACT FROM AUTHOR]