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説明:

Machine learning technology has a great potential to accelerate the discovery of innovative functional materials. We demonstrate a successful discovery of novel high thermal conductivity polymers that was inspired by machine-learning-assisted polymer chemistry. The achievement was made possible by the interplay of a machine intelligence trained with a polymer database called PoLyInfo. Using a Bayesian molecular design algorithm trained to recognize quantitative structure-property relationships on thermal conductivity and other targeted polymeric properties, we identified thousands of promising designed polymers. Three were selected for monomer synthesis and polymerization based on chemical insights on their potential for further processing in real applications.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: machine learning, polymer, thermal conductivity, molecular design

刊行年月日: 2019-06-21

出版者: Springer Science and Business Media LLC

掲載誌:

• npj Computational Materials (ISSN: 20573960) vol. 5 66

研究助成金:

• MEXT | Japan Science and Technology Agency
• MEXT | Japan Society for the Promotion of Science 15H02672
• MEXT | Japan Society for the Promotion of Science JP18K18017
• MEXT | Japan Society for the Promotion of Science 16H06439

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1038/s41524-019-0203-2

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更新時刻: 2024-11-21 16:31:23 +0900

MDRでの公開時刻: 2024-11-21 16:31:24 +0900