Publication date: Oct 22, 2020
This dataset provides DFT (density functional theory as implemented in VASP, Vienna Ab Initio Simulation Package) calculations for pure Magnesium. It was designed by Binglun Yin, Markus Stricker and William A. Curtin for fitting a neural network potential with Behler-Parrinello symmetry functions. Binglun Yin carried out the calculation. It corresponds to a dataset that is commonly used to fit interatomic potentials for mechanics applications and includes structure-energy relationships for structures used to calculate: 1. Bulk properties 2. Generalized stacking fault energies 3. Decohesion and relaxed surfaces 4. Dimer 5. Corner and rod geometries 6. Vacancy formation energy
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|35.6 MiB||Dataset of VASP inputs and calculations.|
|119.3 KiB||The neural-network potential for magnesium.|
|1.4 KiB||README file.|
|2020.129 (version v2) [This version]||Oct 22, 2020||DOI10.24435/materialscloud:8f-1s|
|2020.0046/v1 (version v1)||Apr 27, 2020||DOI10.24435/materialscloud:2020.0046/v1|