As metal–organic frameworks move towards practical application, data for an expanded range of physical properties are needed. Molecular-level modelling and data science can play an important role.
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References
Nat. Chem. 8, 987 (2016).
Freund, R. et al. Angew. Chem. Int. Ed. 60, 23975–24001 (2021).
Mu, B. & Walton, K. S. J. Phys. Chem. C 115, 22748–22754 (2011).
Kloutse, F. A., Zacharia, R., Cossement, D. & Chachine, R. Micropor. Mesopor. Mater. 217, 1–5 (2015).
Babaei, H. et al. Nat. Commun. 11, 4010 (2020).
Moosavi, S. M. et al. Nat. Mater. https://doi.org/10.1038/s41563-022-01374-3 (2022).
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R.Q.S. has a financial interest in the start-up company NuMat Technologies, which is commercializing metal–organic frameworks.
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Snurr, R.Q. Machine learning heat capacities. Nat. Mater. 21, 1342–1343 (2022). https://doi.org/10.1038/s41563-022-01410-2
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DOI: https://doi.org/10.1038/s41563-022-01410-2
