Supplementary files for a computational study on stabilizing hexagonal boron nitride and silicon carbide heterostructures with vacancies and transition-metal atoms. The dataset includes MP4 and PDF files totaling 218.6 MB, authored by Arsalan Hashemi and last updated in April 2026. The work uses density-functional theory and machine-learning molecular dynamics to investigate interlayer bonding and single-atom trapping.
Use Cases
- Validate computational findings on vacancy-induced covalent bonding based on the provided supplementary PDF files.
- Analyze transition-metal atom trapping dynamics based on the machine-learning molecular dynamics results mentioned in the description.
- Study principles for stabilizing isolated single-metal atoms based on the described binding mechanisms.
- Reproduce figures and data from the associated research article based on the supplementary file formats.
Strengths
- Files are provided in multiple formats (MP4, PDF) totaling 218.6 MB.
- Dataset is associated with a peer-reviewed computational study on heterostructure stabilization.
- Released under a permissive CC BY 4.0 license for reuse.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- figshare, authored by Arsalan Hashemi.
- Collection Method
- Generated via density-functional theory calculations and machine-learning molecular dynamics.
- Time Range
- null
- Freshness
- Last updated 2026-04-15 08:08:48; freshness should be verified.
- Geography
- null