Thesis
Atomistic machine learning for modelling disordered tetrahedral networks
- Abstract:
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Disordered tetrahedral networks are pervasive across a broad range of chemically diverse materials, including elemental solids, inorganic glasses, metal–organic frameworks, and liquids. While many such systems lack long-range order, some can exhibit extended correlations that resemble those found in crystalline solids. This structural complexity and variability pose significant challenges for computational modelling, particularly due to the presence of local heterogeneity and the breakdown of...
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- Files:
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(Preview, Dissemination version, pdf, 21.0MB, Terms of use)
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Authors
Contributors
+ Deringer, V
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Chemistry
- Sub department:
- Inorganic Chemistry
- Role:
- Supervisor
+ Martelli, F
- Role:
- Supervisor
+ Engineering and Physical Sciences Research Council
More from this funder
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- MSD2021 1160299
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
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English
- Keywords:
- Subjects:
- Deposit date:
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2026-04-22
- ARK identifier:
Terms of use
- Copyright holder:
- Zoé Faure Beaulieu
- Copyright date:
- 2026
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