Iron Nanoparticle Database with DFT and GAP Energy Comparisons

A database of Fe nanoparticles generated with a Gaussian Approximation Potential interatomic potential. The data includes energies computed with multiple methods including PAW-PBE DFT and several GAP and EAM potentials, and surface motif analysis for each particle. The dataset was created by Richard Jana and Miguel A. Caro of Aalto University, with support from the Academy of Finland and computational resources from CSC and Aalto University.

Use Cases

• Benchmarking the accuracy of different interatomic potentials (GAP, EAM) against DFT calculations based on nanoparticle energies.
• Analyzing surface structure similarities to pristine crystal surfaces based on SOAP descriptor comparisons mentioned in the description.
• Training or validating machine learning potentials for iron nanoparticles using the provided GAP-generated structures and reference data.

Strengths

• Includes energy calculations from five distinct computational methods (PAW-PBE DFT, two GAP potentials, two EAM potentials) for direct comparison.
• Provides surface structure analysis via SOAP descriptors and visualizations in MP4 format for each nanoparticle.

Limitations

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

Provenance

Source

paperswithcode

Collection Method

Generated computationally using a GAP interatomic potential for iron, with subsequent energy calculations using DFT and other potentials.