Research data supporting "Data-driven learning and prediction of inorganic crystal structures"
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Deringer, V., Proserpio, D. M., Csanyi, G., & Pickard, C. (2018). Research data supporting "Data-driven learning and prediction of inorganic crystal structures" [Dataset]. https://doi.org/10.17863/CAM.25572
This dataset contains potential parameter files (*.xml) for the different generations of GAP-RSS interatomic potential models described in the article, as well as structural information and DFT-computed reference and testing databases.
Crystal structure data can be read, visualised, and processed with a range of tools - including, but not restricted to, the free ASE and QUIP/quippy environments. GAP parameter files can be read by, and used with, ASE, QUIP/quippy, and LAMMPS. Additional information on their use can be found at www.libatoms.org.
machine learning, materials modelling, random structure searching, phosphorus
Publication Reference: https://doi.org/10.1039/C8FD00034D
This record's DOI: https://doi.org/10.17863/CAM.25572
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/