Discovery of polymer electret material via de novo molecule generation and functional group enrichment analysis

We designed a high-performance polymer electret material using a deep-learning-based de novo molecule generator. By statistically analyzing the enrichment of the functional groups of the generated molecules, the hydroxyl group was determined to be crucial for enhancing the electron gain energy. Inco...

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Bibliographic Details
Published in:Applied physics letters 2021-05, Vol.118 (22)
Main Authors: Zhang, Yucheng, Zhang, Jinzhe, Suzuki, Kuniko, Sumita, Masato, Terayama, Kei, Li, Jiawen, Mao, Zetian, Tsuda, Koji, Suzuki, Yuji
Format: Article
Language:English
Subjects:
Applied physics
Artificial intelligence
Chemical synthesis
Commercialization
Electrets
Energy harvesting
Enrichment
Functional groups
Hydroxyl groups
Knowledge acquisition
Machine learning
Polymers
Vinyl ethers
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Summary:We designed a high-performance polymer electret material using a deep-learning-based de novo molecule generator. By statistically analyzing the enrichment of the functional groups of the generated molecules, the hydroxyl group was determined to be crucial for enhancing the electron gain energy. Incorporating such acquired knowledge, we designed a molecule using cyclic transparent optical polymer (CYTOP; perfluoro-3-butenyl-vinyl ether). The molecule was synthesized, and its surface potential for a 15-μm-thick film is kept at −3 kV for more than 800 h. Its performance was significantly better than all commercialized CYTOP polymer electrets, indicating great potential for its application in vibration-based energy harvesting. Our results demonstrate the application of machine learning in polymer electret design and confirm the combination of molecule generation and functional group enrichment analysis to be a promising chemical discovery method achieved via human–artificial intelligence collaboration.
ISSN:0003-6951
1077-3118
DOI:10.1063/5.0051902