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VASP DFT calculation skill for electronic structure, geometry optimization, and property prediction of nanomaterials
---
name: vasp-dft-executor-9f73f61a1a28
description: VASP DFT calculation skill for electronic structure, geometry optimization, and property prediction of nanomaterials
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: computational
priority: high
phase: 6
tools-libraries:
- VASP
- VASPKIT
- pymatgen
- ASE
graph:
domains: [domain:nanotechnology]
skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-engineer]
---
# VASP DFT Executor
## Purpose
The VASP DFT Executor skill provides density functional theory calculation capabilities using VASP for nanomaterial property prediction, enabling electronic structure analysis, geometry optimization, and materials property computation.
## Capabilities
- Input file generation (INCAR, POSCAR, KPOINTS, POTCAR)
- Geometry optimization
- Electronic band structure calculation
- Density of states analysis
- Formation energy calculation
- Optical property prediction
## Usage Guidelines
### DFT Calculation Workflow
1. **Input Preparation**
- Generate structure files
- Select appropriate pseudopotentials
- Set convergence parameters
2. **Calculation Execution**
- Monitor convergence
- Check for errors
- Manage computational resources
3. **Result Analysis**
- Extract electronic properties
- Analyze band structure
- Calculate derived properties
## Process Integration
- DFT Calculation Pipeline for Nanomaterials
- Multiscale Modeling Integration
- Machine Learning Materials Discovery Pipeline
## Input Schema
```json
{
"structure_file": "string (POSCAR/CIF)",
"calculation_type": "relax|static|band|dos|optical",
"functional": "PBE|HSE06|SCAN",
"kpoint_density": "number",
"encut": "number (eV)"
}
```
## Output Schema
```json
{
"total_energy": "number (eV)",
"bandgap": "number (eV)",
"formation_energy": "number (eV/atom)",
"optimized_structure": "string (CONTCAR)",
"electronic_properties": {
"dos_file": "string",
"band_file": "string"
},
"convergence": {
"energy_converged": "boolean",
"force_converged": "boolean"
}
}
```
---
name: vasp-dft-executor-9f73f61a1a28
description: VASP DFT calculation skill for electronic structure, geometry optimization, and property prediction of nanomaterials
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: nanotechnology
domain: science
category: computational
priority: high
phase: 6
tools-libraries:
- VASP
- VASPKIT
- pymatgen
- ASE
graph:
domains: [domain:nanotechnology]
skillAreas: [skill-area:mathematical-reasoning, skill-area:physics-simulation, skill-area:data-analysis]
workflows: [workflow:experiment-design]
roles: [role:research-engineer]
---
# VASP DFT Executor
## Purpose
The VASP DFT Executor skill provides density functional theory calculation capabilities using VASP for nanomaterial property prediction, enabling electronic structure analysis, geometry optimization, and materials property computation.
## Capabilities
- Input file generation (INCAR, POSCAR, KPOINTS, POTCAR)
- Geometry optimization
- Electronic band structure calculation
- Density of states analysis
- Formation energy calculation
- Optical property prediction
## Usage Guidelines
### DFT Calculation Workflow
1. **Input Preparation**
- Generate structure files
- Select appropriate pseudopotentials
- Set convergence parameters
2. **Calculation Execution**
- Monitor convergence
- Check for errors
- Manage computational resources
3. **Result Analysis**
- Extract electronic properties
- Analyze band structure
- Calculate derived properties
## Process Integration
- DFT Calculation Pipeline for Nanomaterials
- Multiscale Modeling Integration
- Machine Learning Materials Discovery Pipeline
## Input Schema
```json
{
"structure_file": "string (POSCAR/CIF)",
"calculation_type": "relax|static|band|dos|optical",
"functional": "PBE|HSE06|SCAN",
"kpoint_density": "number",
"encut": "number (eV)"
}
```
## Output Schema
```json
{
"total_energy": "number (eV)",
"bandgap": "number (eV)",
"formation_energy": "number (eV/atom)",
"optimized_structure": "string (CONTCAR)",
"electronic_properties": {
"dos_file": "string",
"band_file": "string"
},
"convergence": {
"energy_converged": "boolean",
"force_converged": "boolean"
}
}
```
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.