Dataset Open Access
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
Marchant, George A.;
Caro, Miguel A.;
Karasulu, Bora;
Partay, Livia B.
This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials"
It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.
Files
(209.4 MB)
| Name | Size | |
|---|---|---|
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complementary_data.tar
md5:8352c3ef7af4028353095ff70c7081c8 |
209.4 MB | Download |
79
15
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 79 | 79 |
| Downloads | 15 | 15 |
| Data volume | 3.1 GB | 3.1 GB |
| Unique views | 78 | 78 |
| Unique downloads | 11 | 11 |
Indexed in
- Publication date:
- December 20, 2022
- DOI:
-
- Keyword(s):
- carbon nested sampling machine learning potential gaussian approximation potential phase diagram
- Grants:
-
Academy of Finland:
- Next-generation interatomic potentials to simulate new cellulose-based materials (NEXTCELL) (330488)
-
Sulis: An EPSRC platform for ensemble computing delivered by HPC Midlands+ (EP/T022108/1)
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Novel computational routes to materials discovery (EP/T000163/1)
- Exploring All-Solid-State Batteries using First-Principles Modelling: Effective Computational Strategies towards Better Batteries (EP/T026138/1)
- Related identifiers:
- Continues
- arXiv:2208.09692 (Working paper)
- License (for files):
- Creative Commons Attribution 4.0 International