A reduced data set containing all results discussed in the associated scientific article. The data was authored by Normand Mousseau and last updated on April 25, 2026. It supports the study of internal dissipation using an activation-relaxation technique and a density functional theory-based moment tensor potential.
Use Cases
- Training machine learning potentials for material properties based on DFT-derived data.
- Analyzing the impact of two-level systems on internal energy dissipation mentioned in the description.
- Benchmarking activation-relaxation technique simulations against theoretical models.
- Studying quantum mechanical dissipation mechanisms in amorphous or disordered solids.
Strengths
- Data is directly linked to a peer-reviewed scientific article, providing context.
- Last update timestamp is precisely recorded as 2026-04-25 04:10:36.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and file format are unknown, which may limit suitability assessment.
Provenance
- Source
- Borealis Harvested Dataverse
- Collection Method
- Likely generated from computational simulations using density functional theory and activation-relaxation techniques.
- Time Range
- null
- Freshness
- Last updated 2026-04 25 04:10:36; freshness should be verified.
- Geography
- null