Dataset
W_LML-retrain_bulk_MD_test
Download Dataset XYZ file
| Name | W_LML-retrain_bulk_MD_test |
|---|---|
| Extended ID | W_LML-retrain_bulk_MD_test_OnatOrtnerKermode__DS_0od3fns1ap8a_0 |
| Description | Test set from W_LML-retrain dataset, containing bulk tungsten calculations. The W_LML-retrain dataset contains DFT calculations used in testing a linear-in-descriptor machine learning potential that accounts for dislocation-defect interactions in tungsten. Density functional simulations were performed using VASP. The PBE generalised gradient approximation was used to describe effects of electron exchange and correlation together with a projector augmented wave (PAW) basis set with a cut-off energy of 550 eV. Occupancies were smeared with a Methfessel-Paxton scheme of order one with a 0.1 eV smearing width. The Brillouin zone was sampled with a Monkhorst-Pack k-point grid for the 2D cluster simulations periodic along the dislocation line and a single k-point was used for the calculations with 3D spherical QM regions. The values of these parameters were chosen after a series of convergence tests on forces with a tolerance of a few meV/Å. |
| Authors |
Berk Onat Christoph Ortner James R. Kermode |
| DOI |
10.60732/9d48595f
https://commons.datacite.org/doi.org/10.60732/9d48595f https://doi.datacite.org/dois/10.60732%2F9d48595f https://doi.org/10.60732/9d48595f Cite as: Onat, B., Ortner, C., and Kermode, J. R. "W LML-retrain bulk MD test." ColabFit, 2023. https://doi.org/10.60732/9d48595f. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Elements |
W (100.0%) |
| Number of Data Objects | 8 |
| Number of Configurations | 8 |
| Number of Atoms | 1,996 |
| Links |
https://github.com/marseille-matmol/LML-retrain https://doi.org/10.1016/j.actamat.2023.118734 |
| Configuration Sets by Name | (None) |
| Configuration Sets by ID | (None) |
| Data Objects | |
| ColabFit ID | DS_0od3fns1ap8a_0 |
| Files | colabfitspec.json |
No uploaded content is transferred in ownership from the original creators to ColabFit. All content is distributed under the license specified by its contributor who has stated that he or she has the authority to share it under the specified license.