The designing of new small molecule acceptors (SMAs) for organic solar cells has been a
prominent area of research for many decades. It is challenging to find unique materials due …

Organic solar cells (OSCs) have drawn a lot of interests because of their distinctive qualities,
including flexibility and tunability. In present study, a detailed data-driven framework is …

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 …

Designing compounds for organic solar cells is a hot topic. In the present study, a new
approach is introduced to design acceptor materials for organic solar cells. Building blocks …

Solubility plays a critical role in many aspects of research (drugs to materials). Solubility
parameters are very useful for selecting appropriate solvents/non-solvents for various …

In this study, an intricate machine learning assisted framework is introduced for the
designing of polymer acceptors. Machine learning (ML) models are trained to predict the …

Abstract Machine learning (ML) analysis has gained huge importance among researchers
for predicting multiple parameters and designing efficient donor and acceptor materials …

In this work, we analyzed a data set formed by 566 donor/acceptor pairs, which are part of
organic solar cells recently reported. We explored the effect of different descriptors in …

Indeed, a proper understanding of materials is necessary to get the full benefit from them.
For this purpose, multiscale computational modeling is the ultimate need. For machine …

In recent years, research on the development of organic solar cells has increased
significantly. For the last few years, machine learning (ML) has been gaining the attention of …