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Materials informatics has recently garnered significant attention as a potent tool for the development of a wide array of functional materials. The successful integration of materials informatics into polymer design holds the promise of streamlining the synthesis of polymers with tailored properties, thereby enhancing efficiency and specificity in material engineering. Machine learning is a subset of artificial intelligence (AI), which involves algorithms learning from data to make predictions or decisions. The machine learning, especially Bayesian optimization method is widely used in materials science.
In this paper, we report our recent approach on the search of electrochromic (EC) metallo-supramolecular polymers (MSPs) with the help of materials informatics. Four components were selected among many components of MSPs. Among all the combination of the variations, the selected number of the corresponding MSPs according to an orthogonal table were synthesized. A coloration efficiency (CE) of 1281 cm2/C was obtained compared to our previous work. We found that this method with statistics was useful to find the polymers with better EC properties quickly.

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• In Copyright
  

  ©The Institute of Image Information and Television Engineers and The Society for Information Display

Keyword: electrochromic, coloration efficiency, data-science

Date published: [2024年]

Publisher: International Display Workshops General Incorporated Association

Journal:

• PROCEEDINGS OF THE INTERNATIONAL DISPLAY WORKSHOPS (ISSN: 18832490) vol. 31 p. 1050-1052

Conference: IDW'24 (2024-12-04 - 2024-12-06)

Funding:

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://pub.confit.atlas.jp/en/event/idw2024

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Updated at: 2025-06-01 08:30:20 +0900

Published on MDR: 2025-06-01 08:24:32 +0900