# Discovery of liquid crystalline polymers with high thermal conductivity using machine learning https://mdr.nims.go.jp/datasets/a7f166ae-ceaf-4618-a87f-03cae6b633f2 ## File - [s41524-025-01671-w.pdf](https://mdr.nims.go.jp/filesets/4727ef36-aaab-4444-b668-d794f59caa1f/download) ([Detail](https://mdr.nims.go.jp/filesets/4727ef36-aaab-4444-b668-d794f59caa1f.md)) ## Id a7f166ae-ceaf-4618-a87f-03cae6b633f2 ## Local identifier ## Visibility open_to_public ## State published ## Created at 2025-08-18T01:55:04.695876Z ## Updated at 2025-08-19T03:30:21.553102Z ## Published at 2025-08-19T03:21:35.586278Z ## Doi ## First published url https://doi.org/10.1038/s41524-025-01671-w ## Date published 2025-07-02 ## Recorded date published ## Resource type journal_article ## Manuscript type vor ## Collection ## Title - title: Discovery of liquid crystalline polymers with high thermal conductivity using machine learning title_type: original lang: en ## Description - description: 'It is highly desired to enhance the heat conduction of polymeric heat-dissipating materials. However, the thermal conductivity of polymeric materials is one to three orders of magnitude lower than that of metals or ceramics due to the occurrence of phonon scattering at amorphous portions. Several attempts have been made to overcome this empirical or theoretical limit to increase the heat flow in the orientation axis by liquid crystallizing the polymers. However, the design of polymeric liquid crystals remains largely empirical. In this study, we have developed a machine learning model that can predict with more than 96% accuracy whether or not assembled polymers form a liquid crystalline state with compositional or structural features of any given polymer repeating unit. Exploring the inverse mapping of the model, we identified a comprehensive set of chemical structures for liquid crystalline polyimides. All the synthesized polymers were experimentally confirmed to spontaneously form liquid crystalline structures with their thermal conductivities ranging from 0.722 to 1.26 W/(m ยท K). ' description_type: abstract lang: und ## Creator - name: Hayato Maeda role: author orcid: https://orcid.org/0009-0004-1662-8000 - name: Stephen Wu role: author orcid: https://orcid.org/0000-0002-7847-8106 - name: Rika Marui role: author orcid: https://orcid.org/0009-0009-2506-1196 - name: Erina Yoshida role: author - name: Kan Hatakeyama-Sato role: author - name: Yuta Nabae role: author - name: Shiori Nakagawa role: author - name: Meguya Ryu role: author - name: Ryohei Ishige role: author - name: Yoh Noguchi role: author - name: Yoshihiro Hayashi role: author orcid: https://orcid.org/0000-0002-7650-4083 - name: Masashi Ishii role: author orcid: https://orcid.org/0000-0003-0357-2832 - name: Isao Kuwajima role: author orcid: https://orcid.org/0000-0002-5994-3834 - name: Felix Jiang role: author - name: Xuan Thang Vu role: author - name: Sven Ingebrandt role: author orcid: https://orcid.org/0000-0002-0405-2727 - name: Masatoshi Tokita role: author - name: Junko Morikawa role: author orcid: https://orcid.org/0000-0002-9530-9478 - name: Ryo Yoshida role: author orcid: https://orcid.org/0000-0001-8092-0162 - name: Teruaki Hayakawa role: author ## Contact agent ## Publisher organization: Springer Science and Business Media LLC ## Managing organization ## Keyword - subject: liquid crystalline polymer schema: not_defined - subject: thermal conductivity schema: not_defined - subject: machine learning schema: not_defined - subject: PoLyInfo schema: not_defined ## Rights - identifier: https://creativecommons.org/licenses/by/4.0/ ## Other identifier(s) ## Data origin - data_origin_type: other ## Embargo ## Journal - title: npj Computational Materials issn: '20573960' volume: '11' issue: '1' article_number: '205' ## Conference ## Related item ## Funding - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJSP2106 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJSP2106 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: 21K04828 funder_name: MEXT | Japan Society for the Promotion of Science - identifier: 21K04828 funder_name: MEXT | Japan Society for the Promotion of Science - funder_name: Japan Synchrotron Radiation Research Institute - identifier: hp210264 funder_name: Ministry of Education, Culture, Sports, Science and Technology - identifier: hp210264 funder_name: Ministry of Education, Culture, Sports, Science and Technology - identifier: hp210264 funder_name: Ministry of Education, Culture, Sports, Science and Technology - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: JPMJCR19I3 funder_name: MEXT | Japan Science and Technology Agency - identifier: hp210264 funder_name: Ministry of Education, Culture, Sports, Science and Technology ## Instrument ## Instrument operator ## Instrument managing organization ## Measurement method ## Specimen ## Chemical composition ## Structure for specimen ## Structural feature for specimen ## Specific property for specimen ## Process for specimen treatment ## Computational method ## Energy level/transition state ## Software ## Custom property ## Fileset - id: 4727ef36-aaab-4444-b668-d794f59caa1f filename: s41524-025-01671-w.pdf content_type: application/pdf size: 3197930 md5: 8c626d5e069d4c52a7f1785a8f15aa55 ## Thumbnail fileset_id: 4727ef36-aaab-4444-b668-d794f59caa1f filename: s41524-025-01671-w.pdf