Chemically intuited, large-scale screening of MOFs by machine learning techniques
Source abstract
Abstract
Abstract A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Machine Learning in Materials Science · X-ray Diffraction in Crystallography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (44)
Show source-provided references
1. Furukawa, H., Cordova, K. E., O’Keeffe, M. & Yaghi, O. M. The chemistry and applications of metal-organic frameworks. Science 341, 1230444 (2013).10.1126/science.1230444 ↗
2. Farruseng, D. Metal-Organic Frameworks: Applications from Catalysis to Gas Storage (Wiley-VCH Verlag & Co. KGaA, 2011).10.1002/9783527635856 ↗
3. Kitagawa, S. & Matsuda, R. Chemistry of coordination space of porous coordination polymers. Coord. Chem. Rev. 251, 2490–2509 (2007).10.1016/j.ccr.2007.07.009 ↗
4. Meek, S. T., Greathouse, J. A. & Allendorf, M. D. Metal-organic frameworks: a rapidly growing class of versatile nanoporous materials. Adv. Mater. 23, 249–267 (2011).10.1002/adma.201002854 ↗
5. Suh, M. P., Park, H. J., Prasad, T. K. & Lim, D.-W. Hydrogen storage in metal-organic frameworks. Chem. Rev. 112, 782–835 (2012).10.1021/cr200274s ↗
6. Furukawa, H. et al. Ultrahigh porosity in metal-organic frameworks. Science 329, 424–428 (2010).10.1126/science.1192160 ↗
7. He, Y., Zhou, W., Qian, G. & Chen, B. Methane storage in metal-organic frameworks. Chem. Soc. Rev. 43, 5657 (2014).10.1039/C4CS00032C ↗
8. Li, J.-R. et al. Carbon dioxide capture-related gas adsorption and separation in metal-organic frameworks. Coord. Chem. Rev. 255, 1791–1823 (2011).10.1016/j.ccr.2011.02.012 ↗
9. Lu, X. et al. Strategies to enhance CO2 storage and separation based on engineering adsorbent materials. J. Mater. Chem. A 3, 12118–12132 (2015).10.1039/C4TA06829G ↗
10. Morris, W., Doonan, C. J. & Yaghi, O. M. Postsynthetic modification of a metal-organic framework for stabilization of a hemiaminal and ammonia uptake. Inorg. Chem. 50, 6853–6855 (2011).10.1021/ic200744y ↗
11. DeCoste, J. B. & Peterson, G. W. Metal-organic frameworks for air purification of toxic chemicals. Chem.Rev. 114, 5695–5727 (2014).10.1021/cr4006473 ↗
12. Khan, N. A., Hasan, Z. & Jhung, S. H. Adsorptive removal of hazardous materials using metal-organic frameworks (MOFs): a review. J. Hazard. Mater. 244-245, 444–456 (2013).10.1016/j.jhazmat.2012.11.011 ↗
13. Zhang, M., Bosch, M., Gentle III, T. & Zhou, H. C. Rational design of metal-organic frameworks with anticipated porosities and functionalities. CrysEngComm 16, 4069 (2014).10.1039/C4CE00321G ↗
14. Mason, J. A., Veenstra, M. & Long, J. R. Evaluating metal-organic frameworks for natural gas storage. Chem. Sci. 5, 32–51 (2014).10.1039/C3SC52633J ↗
15. Nandi, M. & Uyama, H. Exceptional CO2 adsorpbing materials under different conditions. Chem. Rec. 14, 1134–1148 (2014).10.1002/tcr.201402062 ↗
16. Ding, S.-Y. & Wang, W. Covalent organic frameworks (COFs): from design to applications. Chem. Soc. Rev. 42, 548–568 (2013).10.1039/C2CS35072F ↗
17. Zeng, Y., Zou, R. & Zhao, Y. Covalent organic frameworks for CO2 capture. Adv. Mater. 28, 2855–2873 (2016).10.1002/adma.201505004 ↗
18. Chen, B., Yang, Z., Zhu, Y. & Xia, Y. Zeolitic imidazolate framework materials. Recent progress in synthesis and applications. J. Mat. Chem. A 2, 16811–16831 (2014).10.1039/C4TA02984D ↗
19. Pimentel, B. R., Parulkar, A., Zhou, E.-K., Brunelli, N. A. & Lively, R. P. Zeolitic imidazolate frameworks: next-generation materials for energy-efficient gas separations. ChemSusChem 7, 3202–3240 (2014).10.1002/cssc.201402647 ↗
20. Lee, J. S. et al. Understanding small-molecule interactions in metal–organic frameworks: coupling experiment with theory. Adv. Mater. 27, 5785–5796 (2015).10.1002/adma.201500966 ↗
21. Bernales, V. et al. Computationally guided discovery of a catalytic cobalt-decorated metal–organic framework for ethylene dimerization. J. Phys. Chem. C 120, 23576–23583 (2016).10.1021/acs.jpcc.6b07362 ↗
22. Odoh, S. O., Cramer, C. J., Truhlar, D. G. & Gagliardi, L. Quantum-chemical characterization of the properties and reactivities of metal−organic frameworks. Chem. Rev. 115, 6051–6111 (2015).10.1021/cr500551h ↗
23. Evans, J. D. et al. Computational chemistry methods for nanoporous materials. Chem. Mater. 29, 199–212 (2016).10.1021/acs.chemmater.6b02994 ↗
24. Wilmer, C. E. et al. Large-scale screening of hypothetical metal–organic frameworks. Nat. Chem. 4, 83–89 (2012).10.1038/nchem.1192 ↗
25. Witman, M. et al. In silico design and screening of hypothetical MOF-74 analogs and their experimental synthesis. Chem. Sci. 7, 6263–6272 (2016).10.1039/C6SC01477A ↗
26. Xiaoyuan, S. & Khoshgoftaar, T. M. A Survey of collaborative filtering techniques. Adv. Artif. Intell. 4, 1–19 (2009).
27. Deng, L. & Li, X. Machine learning paradigms for speech recognition: an overview, transactions on audio. Speech Lang. Process. 21, 1060–1089 (2013).10.1109/TASL.2013.2244083 ↗
28. Sung, K. K., Poggio, T. Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell.. 20, 39–35 (1998).10.1109/34.655648 ↗
29. Jansen, R. et al. A bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003).10.1126/science.1087361 ↗
30. Käll, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923–925 (2007).10.1038/nmeth1113 ↗
31. Dosenbach, N. U. F. et al. Prediction of individual brain maturity using fMRI. Science 329, 1358–1361 (2010).10.1126/science.1194144 ↗
32. Shipp, M. A. et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat. Med. 8, 68–74 (2002).10.1038/nm0102-68 ↗
33. Ye, Q. H. et al. Predicting hepatitis B virus−positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nat. Med. 9, 416–423 (2003).10.1038/nm843 ↗
34. Orfanoudaki, G., Markaki, M., Chatzi, K., Tsamardinos, I. & Economou, A. MatureP: prediction of secreted proteins with exclusive information from their mature regions. Sci. Rep. 7, 3263 (2017).10.1038/s41598-017-03557-4 ↗
35. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).10.1023/A:1010933404324 ↗
36. Smola, A. J. & Schölkopf, B. A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004).10.1023/B:STCO.0000035301.49549.88 ↗
37. Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).10.1080/00401706.1970.10488634 ↗
38. Fernández-Delgado, M., Cernadas, E., Barro, S. & Amorim, D. Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15, 3133–3181 (2014).
39. Wolpert, D. H. The lack of a priori distinctions between learning algorithms. Neural Comput. 8, 1341–1390 (1996).10.1162/neco.1996.8.7.1341 ↗
40. Mosteller, F., Tukey, J. in Revised Handbook of Social Psychology (eds Lindzey, G. & Aronson E.) 80–203. (Addison Wesley, 1968).
41. Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 2006, 7 (2006).
42. Jensen, D. D. & Cohen, P. R. Multiple comparisons in induction algorithms. Mach. Learn. 38, 309–338 (2000).10.1023/A:1007631014630 ↗
43. Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (CRC press, 1994).10.1201/9780429246593 ↗
44. Tsamardinos, I., Greasidou, E., Tsagris, M. & Borboudakis, G. Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross Validation. Preprint at https://arxiv.org/abs/1708.07180 (2017).