Official Resources
- Homepage: https://github.com/ifilot/pyqint
- Documentation: https://pyqint.readthedocs.io/
- Source Repository: https://github.com/ifilot/pyqint
- License: MIT License
Overview
pyqint is a Python-based, teaching-oriented implementation of the Hartree-Fock method and molecular integrals. It provides a transparent interface to fundamental electronic structure components, making it excellent for learning and prototyping.
Scientific domain: Educational quantum chemistry, molecular integrals
Target user community: Students and educators learning electronic structure theory
Theoretical Methods
- Restricted Hartree-Fock (RHF)
- Molecular integrals over Gaussian basis functions
- Self-Consistent Field (SCF)
- Geometry optimization
- Mulliken population analysis
Capabilities (CRITICAL)
- Overlap integrals
- Kinetic energy integrals
- Nuclear attraction integrals
- Two-electron repulsion integrals (ERI)
- Complete SCF procedure
- Geometry optimization
- Population analysis
- Clear Python interface
- Educational focus
- Modular design
Key Strengths
Educational Value:
- Clear, readable code
- Step-by-step implementation
- Documentation
- Learning-focused
Integral Calculations:
- All one-electron integrals
- Two-electron integrals
- Gaussian basis functions
- Contracted GTOs
SCF Implementation:
- Standard algorithm
- Convergence handling
- Direct and conventional
- Property calculations
Accessibility:
- Pure Python (with Cython)
- Easy installation
- Minimal dependencies
- Cross-platform
Inputs & Outputs
-
Input formats:
- Python API
- Molecular coordinates
- Basis set specifications
-
Output data types:
- Energies
- Orbitals
- Integrals arrays
- Population data
Interfaces & Ecosystem
- NumPy: Array operations
- SciPy: Numerical methods
- Cython: Optional acceleration
- Standard formats: XYZ files
Advanced Features
Integral Engine:
- Obara-Saika scheme
- Recurrence relations
- Contracted Gaussians
- Normalization
SCF Algorithm:
- Convergence acceleration
- Energy calculation
- Orbital output
- Property evaluation
Performance Characteristics
- Speed: Adequate for teaching
- Accuracy: Standard HF accuracy
- System size: Small molecules
- Implementation: Python/Cython
Computational Cost
- Integrals: O(N^4) ERIs
- SCF: Standard HF scaling
- Typical: Small molecules for learning
Limitations & Known Constraints
- Production: Not intended for production
- Methods: HF only
- System size: Small molecules
- Features: Basic functionality
Comparison with Other Codes
- vs PySCF: pyqint simpler, educational
- vs Fermi.jl: Python vs Julia
- vs SlowQuant: Both educational
- Unique strength: Clarity over optimization
Application Areas
Education:
- Quantum chemistry courses
- Understanding HF
- Integral theory
- Code modification
Prototyping:
- Testing ideas
- Algorithm development
- Quick implementations
Verification & Sources
Primary sources:
- GitHub: https://github.com/ifilot/pyqint
- ReadTheDocs: https://pyqint.readthedocs.io/
- Szabo & Ostlund implementations
Confidence: VERIFIED
- Source code: OPEN (GitHub, MIT)
- Documentation: ReadTheDocs
- Educational focus: Yes