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Toward a Database of Hypothetical Zeolite Structures
Abstract
We present a computational method to identify zeolite-like frameworks by sampling a zeolite figure of merit. Monte Carlo methods, including simulated annealing, are used to perform the sampling. We discuss construction of a database of hypothetical zeolite frameworks with this approach and discuss how the database may be used to search for new zeolite structures with specific material properties of interest.
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Cited by
Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials
Willems, T. F., Rycroft, C. H., Kazi, M., Meza, J. C., & Haranczyk, M. (2012) · Microporous and Mesoporous Materials · doi:10.1016/j.micromeso.2011.08.020 · Cited by 1,925
Large-scale screening of hypothetical metal–organic frameworks
Wilmer, C. E., Leaf, M., Lee, C. Y., Farha, O. K., Hauser, B. G., Hupp, J. T., & Snurr, R. Q. (2012) · Nature Chemistry · doi:10.1038/nchem.1192 · Cited by 1,439
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
New Stories of Zeolite Structures: Their Descriptions, Determinations, Predictions, and Evaluations
Li, Y., & Yu, J. (2014) · Chemical Reviews · doi:10.1021/cr500010r · Cited by 603
Extra‐Large‐Pore Zeolites: Bridging the Gap between Micro and Mesoporous Structures
Jiang, J., Yu, J., & Corma, A. (2010) · Angewandte Chemie International Edition · doi:10.1002/anie.200904016 · Cited by 514
Metadata
- Authors
- Earl, D. J., & Deem, M. W.
- Venue
- Industrial & Engineering Chemistry Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-01-06
- Volume
- 45
- Issue
- 16
- Pages
- 5449-5454
- ISSN
- 0888-5885 (ISSN-L)
- 1520-5045
- OpenAlex
- W2047424130
- Cited by
- 176
- References
- 0
- FWCI
- 4.73
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
