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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]

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]

The investigation of adsorption properties of anchor group on photo-anode is foundation in the optimization of dye sensitizers (DS) for solar cells. α-cyanoacrylic acid (CAA) is the typical anchor moiety coupled electronic acceptor in DS. To understand the interaction between rutile TiO 2 photo-anode and DS based upon CAA in dye sensitized solar cells, the atomic configurations, energies and electronic properties of CAA adsorbed on rutile TiO 2 (1 1 0) surface were studied by using density functional theory calculations. The results indicate CAA prefers to dissociatively adsorb on rutile TiO 2 (1 1 0) surface as bidentate configuration in which the O atoms of CAA bind with two adjacent surface five-coordinate Ti atoms along [0 0 1] direction, and the H atom in hydroxyl of CAA adhere to the nearest neighbor surface O atom at bridge site, generating a hydroxyl species. The corresponding adsorption energy is 1.480 eV. The analysis of geometrical parameters, density of states and electron density suggests the bonds between rutile TiO 2 (1 1 0) surface and CAA are formed. The density of states and orbital character at the gamma-point in reciprocal space support that the adsorption of CAA on the surface provides feasible mode for photo-induced electron injection. [ABSTRACT FROM AUTHOR]

LiOsO3 is the first experimentally confirmed polar metal. Previous works suggested that the ground state of LiOsO3 is just close to the critical point of metal–insulator transition. Herein, the electronic state of LiOsO3 is tuned by epitaxial biaxial strain, which undergoes the Slater‐type metal–insulator transition under tensile strain, i.e., the G‐type antiferromagnetism emerges. The underlying mechanism of bandwidth tuning can be extended to its sister compound NaOsO3, which shows an opposite transition from an antiferromagnetic insulator to a nonmagnetic metal under hydrostatic pressure. A feasible route is suggested for the manipulation of magnetism and conductivity of polar metal LiOsO3. [ABSTRACT FROM AUTHOR]

LiOsO3 is the first experimentally confirmed polar metal. Previous works suggested that the ground state of LiOsO3 is just close to the critical point of metal–insulator transition. Herein, the electronic state of LiOsO3 is tuned by epitaxial biaxial strain, which undergoes the Slater‐type metal–insulator transition under tensile strain, i.e., the G‐type antiferromagnetism emerges. The underlying mechanism of bandwidth tuning can be extended to its sister compound NaOsO3, which shows an opposite transition from an antiferromagnetic insulator to a nonmagnetic metal under hydrostatic pressure. A feasible route is suggested for the manipulation of magnetism and conductivity of polar metal LiOsO3. [ABSTRACT FROM AUTHOR]

The structural and magnetic properties of the Cu-doped ZnO (ZnO:Cu) under c-axis pressure were studied using first-principle calculations. It was found that the ZnO:Cu undergoes a structural transition from Wurtzite to Graphite-like structure at a c-axis pressure of 7–8 GPa. This is accompanied by an apparent loss of ferromagnetic stability, indicating a magnetic transformation from a ferromagnetic state to a paramagnetic-like state. Further studies revealed that the magnetic instability is closely related to the variation in crystalline field originated from the structural transition, which is in association with the overlapping of spin–charge density between the Cu2+ and adjacent O2-. [ABSTRACT FROM AUTHOR]