vasp-dft-executor

Solid

VASP DFT calculation skill for electronic structure, geometry optimization, and property prediction of nanomaterials

AI & Automation 814 stars 53 forks Updated today MIT

Install

View on GitHub

Quality Score: 93/100

Stars 20%
97
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
82
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# 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_...

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

Related Skills