Journal
APPLIED PHYSICS LETTERS
Volume 118, Issue 22, Pages -Publisher
AIP Publishing
DOI: 10.1063/5.0051902
Keywords
-
Categories
Funding
- JST CREST [JPMJCR15Q3, JPMJCR19Q1]
- NEDO [P15009]
- SIP (Technologies for Smart Bio-industry and Agriculture), JST CREST [JPMJCR1502]
- JST ERATO [JPMJER1903]
Ask authors/readers for more resources
The research team designed a high-performance polymer electret material using a deep-learning-based molecule generator, identifying the importance of hydroxyl groups in enhancing electron gain energy and successfully creating a molecule with superior performance. The new material shows great potential for application in vibration-based energy harvesting.
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-mu m-thick film is kept at -3kV 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available