Journal article
Mechanical properties of graphene oxide from machine-learning-driven simulations
- Abstract:
- Graphene oxide (GO) materials have complex chemical structures that are linked to their macroscopic properties. Here we show that first-principles simulations with a machine-learned interatomic potential can predict the mechanical properties of GO sheets in agreement with experiment and provide atomistic insights into the mechanisms of strain and fracture. Our work marks a step towards understanding and controlling mechanical properties of carbon-based materials with the help of atomistic machine learning.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.1039/d5cc02753e
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Publisher:
- Royal Society of Chemistry
- Journal:
- Chemical Communications More from this journal
- Publication date:
- 2025-06-16
- Acceptance date:
- 2025-06-16
- DOI:
- EISSN:
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1364-548X
- ISSN:
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1359-7345
- Language:
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English
- Pubs id:
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2133365
- Local pid:
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pubs:2133365
- Source identifiers:
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3055240
- Deposit date:
-
2025-06-26
- ARK identifier:
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Terms of use
- Copyright date:
- 2025
- Licence:
- CC Attribution (CC BY) 3.0
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