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

Abstract

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.

QCforever: Quantum chemistry for everyone

“…Combining QCforever with the black-box optimization algorithm, we can remove this restriction and bias and expand the search space. [27][28][29][30][31][32]…”

“…[27][28][29] In addition, the DNMG proposed to use an material that had never received attention as an electret material. 30 The DNMG becomes a molecular identifier by setting the computed property by QCforever NMR spectrum. 31 In addition to the collaboration with DNMG, QCforever is useful for screening database.…”

De novo creation of a naked eye–detectable fluorescent molecule based on quantum chemical computation and machine learning

“…In addition, DNMGs increase the possibility of discovering new molecules because the search area of a DNMG is not limited in the dataset in contrast to the traditional high-throughput QM and screening with ML models. We also performed functional group enrichment analysis of the molecules produced by ChemTS with QC to maximize the electron gain energy and found important functional groups that are not included in the electret literature ( 25 ).…”

Self-learning entropic population annealing for interpretable materials design

“…6,7 Given a candidate material, an experiment is regarded as a black-box function that returns its property value such as bioactivity, 8 thermal conductivity 9 or electron gain energy. 10 Black-box optimization is an iterative procedure that recommends one material or a batch of materials for experiments at a time. It is expected to nd a material with a favorable property with a minimum number of experiments.…”