Theoretical Modelling of Nitrogen and Sulfur Doping in Graphene Quantum Dots

Density functional theory calculations systematically model the effects of nitrogen and sulfur doping in a graphene quantum dot. Natalia Martsinovich authored this 6.5 GB dataset, which was last updated on 2026-05-12. The data likely contains results from varying dopant chemical nature and positions, such as pyridinic, pyrrolic, graphitic, and amino nitrogen.

Use Cases

• Training machine learning potentials for material property prediction based on dopant type and position.
• Benchmarking computational chemistry methods using the systematic variation of dopant configurations.
• Analyzing structure-property relationships in doped graphene quantum dots based on the described chemical nature of dopants.

Strengths

• Dataset size is 6.5 GB, suggesting a substantial volume of computational results.
• The description specifies a systematic study varying dopant chemical nature and positions, indicating a structured experimental design.

Limitations

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

Provenance

Source

Natalia Martsinovich

Collection Method

Density functional theory calculations.

Freshness

Last updated 2026-05-12 11:19:31; freshness should be verified.