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Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to cheaper prediction of power conversion efficiencies. In the present work, machine learning was used to predict power conversion efficiencies. Experimental data were collected from the literature to feed the machine learning models. A detailed data visualization analysis was performed to study the trends of the dataset. The relationship between descriptors and power conversion efficiency was quantitatively determined by Pearson correlations. The importance of features was also determined using feature importance analysis. More than 10 machine learning models were tried to find better models. Only the two best models (random forest regressor and bagging regressor) were selected for further analysis. The prediction ability of these models was high. The coefficient of determination (R2) values for the random forest regressor and bagging regressor models were 0.892 and 0.887, respectively. The Shapley additive explanation (SHAP) method was used to identify the impact of descriptors on the output of models.

Photovoltaics offers one of the most promising routes to generate electricity in a clean way. As an emerging technology in photovoltaics, organic solar cells (OSC) have attracted a great deal of attention owing to their potential low-cost, lightweight, flexibility and solution processability. Although power conversion efficiencies above 12% have been achieved at this date, there is a great interest for new ideal materials to further improve the PCEs and address device durability, which are major concerns for the commercialization of this technology. The main objective of this thesis is to design and synthesize phenothiazine-based conjugate small molecules and explore their use as electron donor components in OSCs. Phenothiazine is a non-planar moiety with unusual “butterfly” type of geometry, which is known to reduce molecular aggregation and intermolecular excimer formation. In the first study of this thesis, a small molecule based on a cyano-arylenevinylene building block with deep HOMO level was prepared. Although a high open-circuit voltage of 1.0 V was achieved, the tendency of the small molecule to crystallize in the active layer at a higher temperature and with time hindered the attainment of an optimal phase morphology required for the achievement of a higher efficiency. In the second and third studies, phenothiazine was used as a π-system bridge and as a core unit to construct small molecules based on symmetric and asymmetric frameworks with varying terminal electron-withdrawing groups. The electron-withdrawing property of the terminal units was found to have a significant influence on the optical absorption properties, electronic energy levels, molecular ordering, charge carrier mobility and morphology of the resulting active layers. In the fourth study, side-chain modification of the phenothiazine unit of symmetrically configured small molecules with an oxygen-containing (methoxyethoxy ethyl) side chain resulted in the enhancement of the dielectric constant. Although absorption properties were unchanged in solution, a dense π-π stacking was observed in the solid state. In summary, it is demonstrated that phenothiazine is a promising candidate and worth exploring donor material for OSCs. Its versatility as a π-linker and as a central core unit in symmetric and asymmetric configurations has been explored. The use of nonplanar building blocks such as phenothiazine for the construction of donor materials is an interesting strategy for controlling molecular aggregation and difficult solution processability of small molecules if it is combined with a judiciously designed conjugate backbone.