Materials design by evolutionary optimization of functional groups in metal-organic frameworks
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Machine learning is used to optimize functional groups of metal-organic frameworks for a specific application.
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- Authors
- Collins, S. P., Daff, T. D., Piotrkowski, S. S., & Woo, T. K.
- Affiliations
- University of Ottawa (CA)
- Corresponding author
- Woo, Tom K.
- Venue
- Science Advances
- Published
- 2016-11-04
- Volume
- 2
- Issue
- 11
- Pages
- e1600954-e1600954
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC5262444
- OpenAlex
- W2554191423
- Cited by
- 107
- References
- 39
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 7 · 2023: 13 · 2022: 21 · 2021: 15 · 2020: 11 · 2019: 14 · 2018: 8 · 2017: 4
- FWCI
- 3.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Science Advances (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc)
- Apollo
- Topics
- Physical Sciences › Chemistry › Inorganic Chemistry › Metal-Organic Frameworks: Synthesis and Applications
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Materials Chemistry › Covalent Organic Framework Applications
- Keywords
- metal-organic frameworks, evolutionary optimization, adsorption enhancement, materials design
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