Digital twins of self-driving chemistry laboratories may help reduce reliance on costly real-world experimentation and enable the testing of hypothetical automated workflows in silico.
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References
Tao, F., Zhang, H. & Zhang, C. Nat. Comput. Sci. 4, 169–177 (2024).
Mihai, S. et al. IEEE Commun. Surv. Tutor. 24, 2255–2291 (2022).
Ferrari, A. & Willcox, K. Nat. Comput. Sci. 4, 178–183 (2024).
Kim, S. & Heo, S. Nat. Commun. 15, 1561 (2024).
Burger, B. et al. Nature 583, 237–241 (2020).
Szymanski, N. J. et al. Nature 624, 86–91 (2023).
Darvish, K. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-025-00924-4 (2025).
Wen, B., Yang, W., Kautz, J. & Birchfield, S. Foundationpose: Unified 6D pose estimation and tracking of novel objects. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 17868–17879 (IEEE/CVF, 2024).
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Y.Z. thanks the support of grants from Florida State University.
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Zhao, T., Zeng, Y. Digital twins for self-driving chemistry laboratories. Nat Comput Sci 6, 15–16 (2026). https://doi.org/10.1038/s43588-025-00908-4
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DOI: https://doi.org/10.1038/s43588-025-00908-4
