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Informatics colourizes polymers

Nature Reviews Chemistry volume 7, pages 232–233 (2023)Cite this article

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Informatics approaches play an increasingly important role in accelerating the advances of modern materials science. A recent study reports the development of predictive machine learning models to guide the de novo design of through-space charge transfer polymers with full-colour-tunable emission.

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Fig. 1: Machine-learning-guided design of emissive polymers with through-space charge transfer (TSCT) properties.

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Authors and Affiliations

  1. Department of Chemistry, The University of Illinois Urbana-Champaign, Champaign, IL, USA

    Xiaolin Liu

  2. Key Laboratory of Functional Polymer Materials of Ministry of Education, State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin, China

    Chunlei Zhu

  3. School of Science and Engineering, Shenzhen Institute of Aggregate Science and Technology, The Chinese University of Hong Kong, Shenzhen, Guangdong, China

    Ben Zhong Tang

Authors
  1. Xiaolin Liu
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  2. Chunlei Zhu
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  3. Ben Zhong Tang
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Correspondence to Xiaolin Liu, Chunlei Zhu or Ben Zhong Tang.

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Liu, X., Zhu, C. & Tang, B.Z. Informatics colourizes polymers. Nat Rev Chem 7, 232–233 (2023). https://doi.org/10.1038/s41570-023-00484-z

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