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Machine learning (ML) analysis has gained huge importance among researchers for predicting multiple parameters and designing efficient donor and acceptor materials without experimentation. Data are collected from literature and subsequently used for predicting impactful properties of organic solar cells such as power conversion efficiency (PCE) and energy levels (HOMO/LUMO). Importantly, out of various tested models, hist gradient boosting (HGB) and the light gradient boosting (LGBM) regression models revealed better predictive capabilities. To achieve the prediction effectively, the selected (best) ML regression models are further tuned. For the prediction of PCE (test set), the LGBM shows the coefficient of determination (R2) value of 0.787, which is higher than HGB (R2 = 0.680). For the prediction of HOMO (test set), the LGBM shows R2 value of 0.566, which is higher than HGB (R2 = 0.563). However, for the prediction of LUMO (test set), the LGBM shows R2 value of 0.605, which is lower than HGB (R2 = 0.606). Among the three predicted properties, prediction ability is higher for PCE. These models help to predict the efficient acceptors in a short time and less computational cost. [ABSTRACT FROM AUTHOR]

[Display omitted] • Computer assisted exploration of new chemical space designing organics molecules. • Synthetic accessibility informed designing of organic solar cells. • Potential of synthetic ability can be used for virtual screening. Modern computer-assisted synthesis offers solid support for easy virtual screening of easily synthesizable compounds. By using the synthetic accessibility score this restriction may be bypassed. In the present work, a chemical space of organic semi-conductors is generated. Synthetic accessibility of generated organic semi-conductors is studied using Synthetic accessibility (SA) score and Synthetic Bayesian classifier (SYBA) score. These methods are very fast. A significance difference is observed between results obtained from two groups. The distribution of predicted scores using two methods is also different. More research is required in this field. Generated organic semi-conductors are screened on the basis of calculated scores. This study is indicating the potential of synthetic accessibility scores in the virtual screening. These faster methods are ideal for large scale screening. [ABSTRACT FROM AUTHOR]

[Display omitted] • The key parameters of HOMO, LUMO, and λ max values associated with the organic molecules was utilized to perform machine learning (ML) analysis. • The dataset of each parameter was utilized to train the model and the validity of the models was tested against the test dataset. • The Hist gradient boosting (HGB) model exhibited best results for the prediction of the understudy parameters. • The fabrication of high-performance organic solar cells (OSCs) devices was also suggested by using the ML models. 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 (λ max) 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 λ max 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. [ABSTRACT FROM AUTHOR]