• Wu, Jianchang
• Kasian, Olga
• Lahn, Leopold
• Brabec, Christoph J.
• Luo, Junsheng
• Barabash, Anastasia
• Hauch, Jens A.
• Wang, Yunuo
• Zhao, Yicheng
• Reiser, Patrick
• Guldi, Dirk M.
• Pérez-Ojeda, M. Eugenia
• Friederich, Pascal
• Torresi, Luca
• Hu, Manman
• Seok, Sang Il
• Xie, Zhiqiang
• Zhang, Jiyun
• Rocha-Ortiz, Juan S.

Abstract

High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules provide a possibility for quick material discovery and machine learning based on experimental data. However, the diversity of the molecular structure leads to the complexity of molecular properties, such as solubility, polarity, and crystallinity, which poses great challenges to solution processing and purification. Here, we first report an integrated system for the high-throughput synthesis, purification, and characterization of molecules with a large variety. Based on the principle “Like dissolves like,” we combine theoretical calculations and a robotic platform to accelerate the purification of those molecules. With this platform, a material library containing 125 molecules and their optical-electronic properties was built within a timeframe of weeks. More importantly, the high repeatability of recrystallization we design is a reliable approach to further upgrading and industrial production.

Topics

• impedance spectroscopy
• semiconductor
• recrystallization
• crystallinity
• molecular structure
• machine learning
• solution processing