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What Are the Best Materials To Separate a Xenon/Krypton Mixture?
Open accessAbstract
Accelerating progress in the discovery and deployment of advanced nanoporous materials relies on chemical insight and structure–property relationships for rational design. Because of the complexity of this problem, trial-and-error is heavily involved in the laboratory today. A cost-effective route to aid experimental materials discovery is to construct structure models of nanoporous materials in silico and use molecular simulations to rapidly test them and elucidate data-driven guidelines for rational design. For example, highly tunable nanoporous materials have shown promise as adsorbents for separating an industrially relevant gaseous mixture of xenon and krypton. In this work, we characterize, screen, and analyze the Nanoporous Materials Genome, a database of about 670 000 porous material structures, for candidate adsorbents for xenon/krypton separations. For over half a million structures, the computational resources required for a brute-force screening using grand-canonical Monte Carlo simulations of Xe/Kr adsorption are prohibitive. To overcome the computational cost, we used a hybrid approach combining machine learning algorithms (random forests) with molecular simulations. We compared the results from our large-scale screening with simple pore models to rationalize the strong link between pore size and selectivity. With this insight, we then analyzed the anatomy of the binding sites of the most selective materials. These binding sites can be constructed from tubes, pockets, rings, or cages and are often composed of nondiscrete chemical fragments. The complexity of these binding sites emphasizes the importance of high-throughput computational screenings to identify optimal materials for a given application. Interestingly, our screening study predicts that the two most selective materials in the database are an aluminophosphate zeolite analogue and a calcium based coordination network, both of which have already been synthesized but not yet tested for Xe/Kr separations.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot: An International Journal · doi:10.1108/ir.1999.04926fae.001 · Cited by 38,662
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
van der Waals Volumes and Radii
Bondi, A. (1964) · The Journal of Physical Chemistry · doi:10.1021/j100785a001 · Cited by 19,350
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., & Skiff, W. M. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00051a040 · Cited by 10,557
Classification and regression trees
Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053
(2012) · Cited by 9,062
Porous, Crystalline, Covalent Organic Frameworks
Côté, A. P., Benin, A. I., Ockwig, N. W., O'Keeffe, M., Matzger, A. J., & Yaghi, O. M. (2005) · Science · doi:10.1126/science.1120411 · Cited by 8,357
Cited by
Chung, Y. G., Haldoupis, E., Bucior, B. J., Haranczyk, M., Lee, S., Zhang, H., Vogiatzis, K. D., Milisavljevic, M., Ling, S., Camp, J. S., et al. (2019) · Journal of Chemical & Engineering Data · doi:10.1021/acs.jced.9b00835 · Cited by 863
Colón, Y. J., Gómez-Gualdrón, D. A., & Snurr, R. Q. (2017) · Crystal Growth & Design · doi:10.1021/acs.cgd.7b00848 · Cited by 321
Chung, Y. G., Gómez-Gualdrón, D. A., Li, P., Leperi, K. T., Deria, P., Zhang, H., Vermeulen, N. A., Stoddart, J. F., You, F., Hupp, J. T., et al. (2016) · Science Advances · doi:10.1126/sciadv.1600909 · Cited by 321
Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks
Bucior, B. J., Bobbitt, N. S., Islamoglu, T., Goswami, S., Gopalan, A., Yildirim, T., Farha, O. K., Bagheri, N., & Snurr, R. Q. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c8me00050f · Cited by 308
Lee, S., Kim, B., Cho, H., Lee, H., Lee, S. Y., Cho, E. S., & Kim, J. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c02471 · Cited by 213
Metadata
- Authors
- Simon, C. M., Mercado, R., Schnell, S. K., Smit, B., & Haranczyk, M.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-06-11
- Volume
- 27
- Issue
- 12
- Pages
- 4459-4475
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W1810766688
- Cited by
- 275
- References
- 89
- FWCI
- 8.53
- Type
- journal article
- Language
- en
- Open access
- green
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