Thesis
Optimising machine-learning interatomic potentials for the study of disordered functional materials
- Abstract:
-
Machine-learning interatomic potentials (MLIPs) are a powerful tool for studying the properties of materials via atomistic simulation. The modelling of dynamical processes and disordered materials requires large time- and length-scale simulations for modelling relevant to the processes as observed in experiments, making MLIPs the best choice for accurate simulations.
Expand abstract
This thesis explores how the application of hyperparameter optimisation to MLIPs improves our ability to systematically...
Actions
Access Document
- Files:
-
-
(Preview, Dissemination version, pdf, 30.0MB, Terms of use)
-
Authors
Contributors
+ Deringer, V
- Institution:
- University of Oxford
- Division:
- MPLS
- Department:
- Chemistry
- Sub department:
- Inorganic Chemistry
- Role:
- Supervisor
+ UK Research and Innovation
More from this funder
- Funder identifier:
- https://ror.org/001aqnf71
- Funding agency for:
- Deringer, V
- Grant:
- EP/X016188/1
- Programme:
- Horizon Europe Guarantee
- DOI:
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
- Language:
-
English
- Subjects:
- Deposit date:
-
2025-12-13
- ARK identifier:
Terms of use
- Copyright holder:
- Daniel Thomas du Toit
- Copyright date:
- 2025
- Licence:
- CC Attribution (CC BY) 3.0
If you are the owner of this record, you can report an update to it here: Report update to this record