Data for "Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential"
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Mocanu, F., & Konstantinou, K. (2018). Data for "Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential" [Dataset]. https://doi.org/10.17863/CAM.26412
This data set contains: 1. Trajectories of glass and liquid Ge2Sb2Te5 models generated with the GAP. There are 5 models labeled 001-005 of 315 atoms and one large model of 7,200 atoms labeled 7K. 2. Structural analysis data including: radial distribution functions, rings, voids, tetrahedral angular order parameters. 3. The inter-atomic potential used to carry out the simulations. 4. The crystallization trajectory of one of the GAP models is provided separately due to the size.
Extended xyz files of the structures were generated with the quippy python library (https://github.com/libAtoms/QUIP) from the original LAMMPS (https://lammps.sandia.gov/) trajectories. CSV files were generated with the Pandas python library (https://pandas.pydata.org/) and can be read by any library or software that works with this format. The inter-atomic potential xml files were generated with the QUIP teach_sparse program. (http://www.libatoms.org/gap/gap_download.html) The files are compressed in the common zip format.
GAP, machine-learning, atomic structures, glasses, RDF, rings, voids
Publication Reference: https://doi.org/10.1021/acs.jpcb.8b06476
This record's DOI: https://doi.org/10.17863/CAM.26412
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Licence URL: https://creativecommons.org/licenses/by-sa/4.0/
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