Data mining and library generation to search electron-rich and electron-deficient building blocks for the designing of polymers for photoacoustic imaging.

Photoacoustic imaging is a good method for biological imaging, for this purpose, materials with strong near infrared (NIR) absorbance are required. In the present study, machine learning models are used to predict the light absorption behavior of polymers. Molecular descriptors are utilized to train a variety of machine learning models.

Performance prediction of polymer-fullerene organic solar cells and data mining-assisted designing of new polymers.

Context: Selecting high performance polymer materials for organic solar cells (OSCs) remains a compelling goal to improve device morphology, stability, and efficiency. To achieve these goals, machine learning has been reported as a powerful set of algorithms/techniques to solve complex problems and help/guide exploratory researchers to screen, map, and develop high performance materials. In present work, we have applied machine learning tools to screen data from reported studies and designed new polymer acceptor materials, respectively.