Abstract:
Designing effective materials for organic solar cells (OSCs) is a challenging and time-consuming process. To achieve high performance OSCs, efficient designing/screening of materials is essential. In recent years, machine learning (ML) has captured the attention of the scientific community working on OSCs. In present study, efficiency of building blocks is predicted by using different ML models. Machine learning analysis is performed for predicting power conversion efficiency (PCE) as a dependent variable and molecular descriptors as independent factors. Moreover, similarity analysis (Tanimoto similarity) is used to screen structures based on the similarity between structures present in the databases and reference (given) structures. RDkit is used to calculate Tanimoto index and compare the fingerprints of molecules present within the database with fingerprint of reference/query structure. The monomer of three famous polymer donors PM6, PBT7-Th and PDPP3T are used as reference molecules for similarity analysis. The best buildings blocks are selected based on the results obtained from similarity analysis. The high efficiency screened building units are connected to design new polymers. PCE values of newly designed monomers are predicted using already trained machine learning models. This proposed framework can screen and design effective polymers for OSCs and predict their PCE without any experimentation in minimum time with marginal computational cost. © 2023 Elsevier Ltd
