Official Resources
- Homepage: https://github.com/jjgoings/ccq
- Documentation: In repository
- Source Repository: https://github.com/jjgoings/ccq
- License: MIT License
Overview
ccq is a coupled-cluster code designed for quantum chemistry calculations. It implements standard coupled-cluster methods including CCD, CCSD, CCSDT, and CCSDTQ, providing a clean implementation for research and educational purposes.
Scientific domain: Coupled-cluster quantum chemistry
Target user community: Researchers studying coupled-cluster methods and correlation effects
Theoretical Methods
- Coupled Cluster Doubles (CCD)
- Coupled Cluster Singles and Doubles (CCSD)
- Coupled Cluster Singles, Doubles, Triples (CCSDT)
- Coupled Cluster Singles, Doubles, Triples, Quadruples (CCSDTQ)
- Perturbative corrections
- Reference: RHF
Capabilities (CRITICAL)
- Full CCSD implementation
- Full CCSDT implementation
- Full CCSDTQ implementation
- Clean Python code
- Tensor contraction
- Energy calculations
- Amplitude equations
- Iterative solvers
- Benchmark calculations
Key Strengths
Method Variety:
- Multiple CC truncations
- Full implementation (not approximate)
- High-level correlation
- Benchmark quality
Implementation:
- Clear code structure
- Educational value
- Extensible design
- Python-based
Research Tool:
- Method development
- Testing algorithms
- Comparison studies
- Teaching purposes
Inputs & Outputs
-
Input formats:
- Molecular integrals
- Python API
-
Output data types:
- Correlation energies
- Amplitudes
- Convergence data
Interfaces & Ecosystem
- Integral sources: External integral packages
- NumPy: Array computations
- SciPy: Numerical methods
Performance Characteristics
- Speed: Standard CC scaling
- Accuracy: Full CC methods
- System size: Small molecules
- Memory: CC amplitudes storage
Computational Cost
- CCSD: O(N^6)
- CCSDT: O(N^8)
- CCSDTQ: O(N^10)
- Typical: Benchmarks on small systems
Limitations & Known Constraints
- System size: Limited by CC scaling
- Production: Research/educational focus
- Methods: CC ground state
- Large systems: Not suitable
Comparison with Other Codes
- vs CFOUR/MRCC: ccq educational, production codes optimized
- vs ccpy: Different implementations
- vs PySCF CC: ccq standalone
- Unique strength: Clean full CC implementations
Application Areas
Benchmarking:
- Reference calculations
- Method comparison
- Basis set studies
Education:
- Learning CC theory
- Algorithm understanding
- Code modification
Verification & Sources
Primary sources:
- GitHub: https://github.com/jjgoings/ccq
- CC theory literature
- Research applications
Confidence: VERIFIED
- Source code: OPEN (GitHub, MIT)
- Implementation: Standard CC