Abstract
Organic solar cells have the potential to be the most cost-effective kind of energy. The small molecule acceptors (SMAs) and their chemical structure influence the efficiency of OSCs. This study presents a multidimensional methodology for the screening of organic semiconductors. Over 40 machine learning (ML) models are tested to identify the optimal ML model. The LGBM regressor model is considered to be the finest approach. Its hyperparameters are tuned to enhance their predictive capabilities. The design of 5000 SMAs are achieved by Breaking Retro-synthetically Interesting Chemical Substructures (BRICS) approach. Experimental chemists may employ ML to select the most suitable SMAs.
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