The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture
Abstract
In this work, we have developed quantitative structure-property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Metal-organic framework materials as catalysts
Lee, J., Farha, O. K., Roberts, J., Scheidt, K. A., Nguyen, S. T., & Hupp, J. T. (2009) · Chemical Society Reviews · doi:10.1039/b807080f · Cited by 7,969
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
Fernandez, M., Trefiak, N. R., & Woo, T. K. (2013) · The Journal of Physical Chemistry C · doi:10.1021/jp404287t · Cited by 167
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Cited by
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
CO2 Capture and Separations Using MOFs: Computational and Experimental Studies
Yu, J., Xie, L.-H., Li, J.-R., Ma, Y., Seminario, J. M., & Balbuena, P. B. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00626 · Cited by 1,047
Understanding the diversity of the metal-organic framework ecosystem
Moosavi, S. M., Nandy, A., Jablonka, K. M., Ongari, D., Janet, J. P., Boyd, P. G., Lee, Y., Smit, B., & Kulik, H. J. (2020) · Nature Communications · doi:10.1038/s41467-020-17755-8 · Cited by 622
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Metadata
- Authors
- Fernandez, M., Boyd, P. G., Daff, T. D., Aghaji, M. Z., & Woo, T. K.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-08-25
- Volume
- 5
- Issue
- 17
- Pages
- 3056-3060
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W2055974394
- Cited by
- 296
- References
- 4
- FWCI
- 7.45
- Type
- journal article
- Language
- en
- Open access
- closed
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