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Machine learning

Informatics colourizes polymers

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.


  1. Samuel, A. L. Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3, 210–229 (1959).

    Article  Google Scholar 

  2. Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).

    Article  CAS  PubMed  Google Scholar 

  3. Ornes, S. Computers take art in new directions, challenging the meaning of “creativity”. Proc. Natl Acad. Sci. USA 116, 4760–4763 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Ju, C.-W., Bai, H., Li, B. & Liu, R. Machine learning enables highly accurate predictions of photophysical properties of organic fluorescent materials: emission wavelengths and quantum yields. J. Chem. Inf. Model. 61, 1053–1065 (2021).

    Article  CAS  PubMed  Google Scholar 

  6. Mei, J., Leung, N. L. C., Kwok, R. T. K., Lam, J. W. Y. & Tang, B. Z. Aggregation-induced emission: together we shine, united we soar! Chem. Rev. 115, 11718–11940 (2015).

    Article  CAS  PubMed  Google Scholar 

  7. Ye, S. et al. Continuous color tuning of single-fluorophore emission via polymerization-mediated through-space charge transfer. Sci. Adv. 7, eabd1794 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ye, S. et al. Machine learning-assisted exploration of a versatile polymer platform with charge transfer-dependent full-color emission. Chem (2023).

    Article  Google Scholar 

  9. Gong, J. et al. ASBase: The universal database for aggregate science. Aggregate 4, e263 (2023).

    CAS  Google Scholar 

  10. Kearnes, S. M. et al. The open reaction database. J. Am. Chem. Soc. 143, 18820–18826 (2021).

    Article  CAS  PubMed  Google Scholar 

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Correspondence to Xiaolin Liu, Chunlei Zhu or Ben Zhong Tang.

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The authors declare no competing interest.

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Liu, X., Zhu, C. & Tang, B.Z. Informatics colourizes polymers. Nat Rev Chem 7, 232–233 (2023).

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