Official Resources
- Source Repository: https://github.com/kavanase/vaspup2.0
- Documentation: https://vaspup2.0.readthedocs.io/
- License: Open source (MIT)
Overview
vaspup2.0 is a Python package for VASP convergence testing. It automates energy, k-point, and cutoff convergence tests with automatic job submission, result extraction, and convergence plotting, streamlining the setup of production VASP calculations.
Scientific domain: VASP convergence testing, calculation setup automation
Target user community: Researchers needing automated convergence testing for VASP DFT calculations
Theoretical Methods
- Energy convergence testing
- k-point convergence testing
- ENCUT convergence testing
- Automatic job submission
- Result extraction and plotting
- Convergence criteria checking
Capabilities (CRITICAL)
- Automated k-point convergence testing
- Automated ENCUT convergence testing
- Energy convergence analysis
- Automatic job generation and submission
- Convergence plotting
- Convergence criteria checking
- VASP input generation
Sources: GitHub repository, JOSS
Key Strengths
Automated Convergence:
- No manual job setup
- Automatic result extraction
- Convergence criteria checking
- Publication-quality plots
Multiple Test Types:
- k-point convergence
- ENCUT convergence
- Energy convergence
- Combined tests
Easy Setup:
- Simple configuration
- Automatic directory structure
- Job submission scripts
- Clear output
Inputs & Outputs
-
Input formats:
- VASP input files (POSCAR, INCAR, KPOINTS, POTCAR)
- Configuration file
-
Output data types:
- Convergence plots
- Energy vs k-points
- Energy vs ENCUT
- Convergence summary
Interfaces & Ecosystem
- VASP: Primary DFT code
- Python: Core language
- Matplotlib: Plotting
- pymatgen: Structure handling
Performance Characteristics
- Speed: Fast (workflow management)
- Accuracy: VASP-level
- System size: Any
- Automation: Full convergence workflow
Computational Cost
- Setup: Seconds
- VASP calculations: Hours (separate)
- Analysis: Seconds
- Typical: Efficient workflow
Limitations & Known Constraints
- VASP only: No QE or other code support
- Convergence only: No production run management
- HPC focused: Designed for cluster use
- Limited analysis: Convergence-focused only
Comparison with Other Codes
- vs Custodian: vaspup2.0 is convergence testing, Custodian is error handling
- vs atomate2: vaspup2.0 is simple convergence, atomate2 is full workflow
- vs VASPKIT: vaspup2.0 is convergence, VASPKIT is general post-processing
- Unique strength: Automated VASP convergence testing with plotting and criteria checking
Application Areas
VASP Setup:
- k-point convergence
- Cutoff energy convergence
- Production calculation setup
- Systematic convergence testing
High-Throughput:
- Batch convergence testing
- Multiple structure convergence
- Consistent convergence criteria
- Database-ready setup
Teaching:
- Convergence demonstration
- Best practices teaching
- VASP workflow learning
- Reproducible setup
Best Practices
Convergence Criteria:
- Use energy convergence < 1 meV/atom
- Check k-point and ENCUT separately
- Use appropriate k-point scheme
- Consider system-specific needs
HPC Setup:
- Configure job scheduler
- Use appropriate queue
- Set reasonable wall times
- Monitor convergence progress
Community and Support
- Open source (MIT)
- ReadTheDocs documentation
- Published in JOSS
- Active development
Verification & Sources
Primary sources:
- GitHub: https://github.com/kavanase/vaspup2.0
- Documentation: https://vaspup2.0.readthedocs.io/
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Documentation: ACCESSIBLE (ReadTheDocs)
- Published methodology: JOSS
- Specialized strength: Automated VASP convergence testing with plotting and criteria checking