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Pure Magnesium DFT calculations for interatomic potential fitting

Binglun Yin1*, Markus Stricker1*, W. A. Curtin1*

1 Laboratory for Multiscale Mechanics Modeling, Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Vaud, Switzerland

* Corresponding authors emails: binglun.yin@epfl.ch, markus.stricker@epfl.ch, william.curtin@epfl.ch
DOI10.24435/materialscloud:8f-1s [version v2]

Publication date: Oct 22, 2020

How to cite this record

Binglun Yin, Markus Stricker, W. A. Curtin, Pure Magnesium DFT calculations for interatomic potential fitting, Materials Cloud Archive 2020.129 (2020), doi: 10.24435/materialscloud:8f-1s.


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

Materials Cloud sections using this data

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File name Size Description
35.6 MiB Dataset of VASP inputs and calculations.
119.3 KiB The neural-network potential for magnesium.
1.4 KiB README file.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.


DFT magnesium metallurgy stacking faults decohesion surfaces elasticity MARVEL/DD2 SNSF EPFL