Research data supporting "Machine learning based interatomic potential for amorphous carbon"
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Deringer, V., & Csanyi, G. (2017). Research data supporting "Machine learning based interatomic potential for amorphous carbon" [Dataset]. https://doi.org/10.17863/CAM.7453
Raw data relevant to the GAP interatomic potential model described in the publication, including output of molecular-dynamics trajectories, DFT reference data, and input files for GAP fitting. Due to the large file sizes, datasets from DFT-based molecular-dynamics simulations ("..._cp2k.tar.gz") and from GAP-based surface simulations ("..._surfaces.tar.gz") are provided as separate archives. The other data, including the GAP fitting input, are included in the main data file.
Data regarding structures, energies, forces, etc. are provided in extended XYZ format, which can be accessed and visualised using a variety of freely available software. The entire dataset is compressed as .tar.gz files which can be extracted using auxiliary software (such as Unix tar).
Gaussian approximation potential (GAP), amorphous carbon, molecular dynamics, density-functional theory (DFT)
Publication Reference: https://www.repository.cam.ac.uk/handle/1810/262316
Isaac Newton Trust (1624(n))
Engineering and Physical Sciences Research Council (EP/K014560/1)
This record's DOI: https://doi.org/10.17863/CAM.7453
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Licence URL: https://creativecommons.org/licenses/by-nc-sa/4.0/