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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References
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).
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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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DOI: https://doi.org/10.1038/s41570-023-00484-z