Pure Magnesium DFT Calculations for Neural Network Potential Fitting

DFT calculations for pure Magnesium were performed using the VASP software package. The dataset was designed by Binglun Yin, Markus Stricker, and William A. Curtin specifically for fitting a neural network potential using Behler-Parrinello symmetry functions. The calculations were carried out by Binglun Yin at École Polytechnique Fédérale de Lausanne.

Use Cases

• Training neural network potentials based on DFT-calculated energies and forces.
• Benchmarking interatomic potential fitting methods using the provided DFT reference data.
• Studying the properties of pure magnesium through atomistic simulation inputs.

Strengths

• Data is generated from first-principles DFT calculations using the established VASP software.
• Dataset has a clear, stated purpose for fitting neural network potentials with specific symmetry functions.

Limitations

• Row count, file formats, and column-level documentation are unknown, limiting suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

École Polytechnique Fédérale de Lausanne

Collection Method

Density functional theory calculations performed with the Vienna Ab Initio Simulation Package (VASP).