Journal article
Coarse-grained <i>versus</i> fully atomistic machine learning for zeolitic imidazolate frameworks
- Abstract:
- phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be "coarse-grained" in hybrid framework materials.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.1MB, Terms of use)
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- Publisher copy:
- 10.1039/d3cc02265j
Authors
+ UK Research and Innovation
More from this funder
- Funder identifier:
- 10.13039/100014013
- Grant:
- UKRI Linacre - The EPA Cephalosporin Scholarship
+ H2020 European Research Council
More from this funder
- Funder identifier:
- 10.13039/100010663
- Grant:
- 788144
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- 10.13039/501100000266
- Grant:
- EP/T517811/1
- Publisher:
- Royal Society of Chemistry
- Journal:
- Chemical Communications More from this journal
- Volume:
- 59
- Issue:
- 76
- Pages:
- 11405-11408
- Publication date:
- 2023-09-21
- DOI:
- EISSN:
-
1364-548X
- ISSN:
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1359-7345
- Language:
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English
- Keywords:
- Pubs id:
-
1526311
- Local pid:
-
pubs:1526311
- Source identifiers:
-
W4386069135
- Deposit date:
-
2026-05-17
- ARK identifier:
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Terms of use
- Copyright date:
- 2023
- Licence:
- CC Attribution (CC BY)
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