# Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

https://mdr.nims.go.jp/datasets/f2b2dca6-0f86-4212-ab79-a332ce6ae4c3

## File

- [s41524-019-0203-2.pdf](https://mdr.nims.go.jp/filesets/a1f7e342-d5cf-43d6-9000-1106f509d5da/download) ([Detail](https://mdr.nims.go.jp/filesets/a1f7e342-d5cf-43d6-9000-1106f509d5da.md))
- [41524_2019_203_MOESM2_ESM.pdf](https://mdr.nims.go.jp/filesets/1d5b3f84-0db4-4a48-a1d6-590f1812923b/download) ([Detail](https://mdr.nims.go.jp/filesets/1d5b3f84-0db4-4a48-a1d6-590f1812923b.md))
- [41524_2019_203_MOESM1_ESM.avi](https://mdr.nims.go.jp/filesets/b63db25a-6798-4038-8cc2-0fda75a08af7/download) ([Detail](https://mdr.nims.go.jp/filesets/b63db25a-6798-4038-8cc2-0fda75a08af7.md))

## Id

f2b2dca6-0f86-4212-ab79-a332ce6ae4c3

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-11-21T05:08:14.884089Z

## Updated at

2024-11-21T07:31:23.987760Z

## Published at

2024-11-21T07:31:24.085425Z

## Doi



## First published url

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

## Date published

2019-06-21

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Machine-learning-assisted discovery of polymers with high thermal conductivity
    using a molecular design algorithm
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Stephen Wu
  role: author
- name: Yukiko Kondo
  role: author
- name: Masa-aki Kakimoto
  role: author
- name: Bin Yang
  role: author
- name: Hironao Yamada
  role: author
- name: Isao Kuwajima
  role: author
  orcid: https://orcid.org/0000-0002-5994-3834
- name: Guillaume Lambard
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079
  organization: National Institute for Materials Science
- name: Kenta Hongo
  role: author
- name: Yibin Xu
  role: author
  orcid: https://orcid.org/0000-0001-8600-8748
- name: Junichiro Shiomi
  role: author
- name: Christoph Schick
  role: author
- name: Junko Morikawa
  role: author
- name: Ryo Yoshida
  role: author

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: machine learning
  schema: not_defined
- subject: polymer
  schema: not_defined
- subject: thermal conductivity
  schema: not_defined
- subject: molecular design
  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: '5'
  article_number: '66'

## Conference



## Related item



## Funding

- funder_name: MEXT | Japan Science and Technology Agency
- identifier: 15H02672
  funder_name: MEXT | Japan Society for the Promotion of Science
- identifier: JP18K18017
  funder_name: MEXT | Japan Society for the Promotion of Science
- identifier: 16H06439
  funder_name: MEXT | Japan Society for the Promotion of Science

## 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: a1f7e342-d5cf-43d6-9000-1106f509d5da
  filename: s41524-019-0203-2.pdf
  content_type: application/pdf
  size: 2360653
  md5: f72a2fe542cef6e904947002d2fc17bf
- id: 1d5b3f84-0db4-4a48-a1d6-590f1812923b
  filename: 41524_2019_203_MOESM2_ESM.pdf
  content_type: application/pdf
  size: 1547538
  md5: 7a4a5de085d4844fb4b4ce483024cbda
- id: b63db25a-6798-4038-8cc2-0fda75a08af7
  filename: 41524_2019_203_MOESM1_ESM.avi
  content_type: video/x-msvideo
  size: 13298972
  md5: 7836b95bc2696afad8df302c39f6909f

## Thumbnail

fileset_id: b63db25a-6798-4038-8cc2-0fda75a08af7
filename: 41524_2019_203_MOESM1_ESM.avi