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.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
Samuel, A. L. Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3, 210–229 (1959).
Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).
Ornes, S. Computers take art in new directions, challenging the meaning of “creativity”. Proc. Natl Acad. Sci. USA 116, 4760–4763 (2019).
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
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).
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).
Ye, S. et al. Continuous color tuning of single-fluorophore emission via polymerization-mediated through-space charge transfer. Sci. Adv. 7, eabd1794 (2021).
Ye, S. et al. Machine learning-assisted exploration of a versatile polymer platform with charge transfer-dependent full-color emission. Chem https://doi.org/10.1016/j.chempr.2022.12.003 (2023).
Gong, J. et al. ASBase: The universal database for aggregate science. Aggregate 4, e263 (2023).
Kearnes, S. M. et al. The open reaction database. J. Am. Chem. Soc. 143, 18820–18826 (2021).
The authors declare no competing interest.
Rights and permissions
About this article
Cite this article
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