PtNi Alloy Datasets: Structures, Energies, and Forces for Machine Learning Potentials

55,982 initially sampled Pt-Ni alloy structures with EAM energies and forces are provided in PtNi_alloy_eam.db. A final set of 6,828 resampled structures with DFT energies and forces calculated by VASP is available in PtNi_alloy_dft.db. Shuang Han from the Technical University of Denmark created this dataset for training neural network potentials and other machine learning models.

Use Cases

• Training neural network potentials based on the provided DFT-calculated energies and forces.
• Fitting other machine learning models for property prediction using the sampled alloy structures.
• Analyzing structural stability of Pt-Ni nanoalloys based on the convex hull vertices and mixing energy data.
• Conducting hybrid MC-MD simulations using the provided input files and scripts.

Strengths

• Includes 55,982 initially sampled structures and a refined set of 6,828 DFT-calculated structures.
• Provides additional properties for stable structures, such as mixing energy, motif, and symmetry axis.
• Comes with a full suite of input files and scripts for simulation, resampling, calculation, and analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the final vertices dataset is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Technical University of Denmark

Collection Method

Structures were sampled and energies/forces calculated using EAM and DFT (VASP) methods.