Designing of novel organic semiconductors materials for organic solar cells: A machine learning assisted proficient pipeline

Abstract

Typical research design associated with organic solar cells (OSCs) is conventionally considered time-consuming and laborious because the selection of the materials as the core, pi-acceptor, and terminal groups required for the engineering of these devices is done via hit and trial methodology. The advanced data-driven approaches, particularly machine learning (ML), have materialized as the robust technique for identifying the organic materials for the fabrication of the OSCs devices. The key parameters of highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), and maximum absorption wavelength (λmax) were selected for developing the ML models. The molecular descriptor associated with each parameter was investigated and the relative contribution of the understudy descriptors in the training of the ML model was studied by using the relative importance test. The Hist gradient boosting (HGB) model exhibited the best results for performing the predictive analysis of all three parameters. Moreover, the chemical database was constructed based on the academic literature to develop the high-performance OSCs devices, and the trained HGB model was applied to predict the HOMO, LUMO, and λmax values for these newly designed OSCs devices. Synthetic accessibility of designed molecules is also predicted which revealed that the suggested new organic molecules can be easily commercialized via experimentation. Highly encouraging results in terms of the understudy key parameters were acquired by this ML approach indicating that the data-driven approaches hold extreme potential for engineering high-performance OSCs devices.