Cited by 107Metal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials ScienceCovalent Organic Framework Applications

Materials design by evolutionary optimization of functional groups in metal-organic frameworks

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Collins, Sean P., Daff, Thomas D., Piotrkowski, Sarah S., Woo, Tom K.·2016·Science Advances

Abstract

Machine learning is used to optimize functional groups of metal-organic frameworks for a specific application.

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Crossref · 10.1126/sciadv.1600954

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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)
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
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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