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Updated: May 11, 2026
Morphology Control for Fully Printable Organic–Inorganic Bulk-heterojunction Solar Cells Based on a Ti-alkoxide and Semiconducting Polymer
Published on: January 10, 2017
Accelerated discovery of polymer donors for organic solar cells through machine learning: From library creation to
Mudassir Hussain Tahir1, Aftab Farrukh2, Faleh Zafer Alqahtany3
1Research Faculty of Agriculture, Field Science Center for Northern Biosphere, Hokkaido University, Sapporo, Hokkaido 060-8589, 060-0811, Japan.
Abstract:
The design of novel polymer donors for organic solar cells has been a major research focus for decades, but discovering unique materials remains challenging due to the high cost of experimentation. In this study, machine learning models are employed to predict power conversion efficiency (PCE), Mordred descriptors are used for model training. Among the four machine learning models evaluated, the gradient boosting regressor emerged as the best-performing model. Additionally, a chemical library of polymer donors was generated and analyzed using various measures. 30 donors with highest PCE are selected and their synthetic accessibility is evaluated. Similarity analysis has indicated much resemblance in selected polymer donors.
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