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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 adsorption of α-cyanoacrylic acid (CAA) on anatase TiO2 (101) and (001) surfaces, including adsorption energies, structures, and electronic properties, have been studied by means of density functional theory calculations in connection with ultrasoft pseudopotential and generalized gradient approximation based upon slab models. The most stable structure of CAA on anatase TiO2 (101) surface is the dissociated bidentate configuration where the cyano N and carbonyl O bond with two adjacent surface Ti atoms along [010] direction and the dissociated H binds to the surface bridging O which connects the surface Ti bonded with carbonyl O. While for the adsorption of CAA on (001) surface, the most stable structure is the bidentate configuration through the dissociation of hydroxyl in carboxyl moiety. The O atoms of carboxyl bond with two neighbor surface Ti along [100] direction, and the H from dissociated hydroxyl interacts with surface bridging O, generating OH species. The adsorption energies are estimated to be 1.02 and 3.25 eV for (101) and (001) surfaces, respectively. The analysis of density of states not only suggests the bonds between CAA and TiO2 surfaces are formed but also indicates that CAA adsorptions on TiO2 (101) and (001) surfaces provide feasible mode for photo-induced electron injection through the interface between TiO2 and CAA. This is resulted from that, compared with the contribution of CAA orbitals in valence bands, the conduction bands which are mainly composed of Ti 3d orbitals have remarkable reduction of the component of CAA orbitals.