The DOI matches its Crossref record; 460 of the 460 references it cites were found in a registry or an index.
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Open accessAbstract
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
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Cited by
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
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
Self-citationRosen, A. S., Iyer, S. M., Ray, D., Yao, Z., Aspuru-Guzik, A., Gagliardi, L., Notestein, J. M., & Snurr, R. Q. (2021) · Matter · doi:10.1016/j.matt.2021.02.015 · Cited by 538
He, H., Zhang, R., Zhang, P., Wang, P., Chen, N., Qian, B., Zhang, L., Yu, J., & Dai, B. (2023) · Advanced Science · doi:10.1002/advs.202205557 · Cited by 432
Programmable Logic in Metal–Organic Frameworks for Catalysis
Shen, Y., Pan, T., Wang, L., Ren, Z., Zhang, W., & Huo, F. (2021) · Advanced Materials · doi:10.1002/adma.202007442 · Cited by 316
Metadata
- Authors
- Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-06-10
- Volume
- 120
- Issue
- 16
- Pages
- 8066-8129
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3000754666
- Cited by
- 599
- References
- 460
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
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