Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 9, Issue 10, Pages 2639-2646Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.8b00635
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Funding
- PRESTO program from the Japan Science and Technology Agency (JST) of Japan [JPMJPR15N6]
- Japan Society for the Promotion of Science (JSPS)
- KAKENHI [JP16H02285]
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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.
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