Computer-Aided Screening of Conjugated Polymers for Organic Solar Cell: Classification by Random Forest

Owing to the diverse
chemical structures, organic photovoltaic
(OPV) applications with a bulk heterojunction framework have greatly
evolved over the last two decades, which has produced numerous organic
semiconductors exhibiting improved power conversion efficiencies (PCEs).
Despite the recent fast progress in materials informatics and data
science, data-driven molecular design of OPV materials remains challenging.
We report a screening of conjugated molecules for polymer–fullerene
OPV applications by supervised learning methods (artificial neural
network (ANN) and random forest (RF)). Approximately 1000 experimental
parameters including PCE, molecular weight, and electronic properties
are manually collected from the literature and subjected to machine
learning with digitized chemical structures. Contrary to the low correlation
coefficient in ANN, RF yields an acceptable accuracy, which is twice
that of random classification. We demonstrate the application of RF
screening for the design, synthesis, and characterization of a conjugated
polymer, which facilitates a rapid development of optoelectronic materials.