PyCI

PyCI is a Python library for configuration interaction (CI) calculations. Part of the TheoChem ecosystem, it provides efficient CI implementations using determinant-based algorithms with optimized Slater-Condon rules. Integrates with Mod…

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Overview

PyCI is a Python library for configuration interaction (CI) calculations. Part of the TheoChem ecosystem, it provides efficient CI implementations using determinant-based algorithms with optimized Slater-Condon rules. Integrates with ModelHamiltonian and FanPy for comprehensive wavefunction studies.

Reference Papers

Reference papers are not yet linked for this code.

Full Documentation

Official Resources

  • Homepage: https://github.com/theochem/pyci
  • Documentation: https://pyci.readthedocs.io/
  • Source Repository: https://github.com/theochem/pyci
  • License: GNU General Public License v3.0

Overview

PyCI is a Python library for configuration interaction (CI) calculations. Part of the TheoChem ecosystem, it provides efficient CI implementations using determinant-based algorithms with optimized Slater-Condon rules. Integrates with ModelHamiltonian and FanPy for comprehensive wavefunction studies.

Scientific domain: Configuration interaction, electron correlation, FCI
Target user community: Researchers performing CI calculations and wavefunction method development

Theoretical Methods

  • Full Configuration Interaction (FCI)
  • Selected Configuration Interaction (sCI)
  • CISD (singles and doubles)
  • CISDT (singles, doubles, triples)
  • Active space CI
  • Heat-bath CI (HCI)
  • Spin-adapted implementations
  • Sparse Hamiltonian methods

Capabilities (CRITICAL)

  • Full CI for small systems
  • Selected CI for larger systems
  • Determinant-based algorithms
  • Efficient Slater-Condon rules
  • Sparse matrix techniques
  • ModelHamiltonian integration
  • FanPy wavefunction interface
  • Custom Hamiltonian support
  • Ground and excited states
  • Spin eigenfunctions

Key Strengths

CI Methods:

  • Multiple truncation levels
  • Selection schemes (HCI)
  • Variational treatment
  • Size-consistency corrections

Efficiency:

  • Optimized Slater-Condon
  • Sparse Hamiltonian storage
  • Davidson diagonalization
  • Memory-efficient schemes

Integration:

  • TheoChem ecosystem
  • Standard integral formats
  • FanPy wavefunctions
  • Model Hamiltonians

Research Flexibility:

  • Custom CI spaces
  • User-defined selections
  • Algorithm testing
  • Method development

Inputs & Outputs

  • Input formats:

    • Python API
    • FCIDUMP integrals
    • ModelHamiltonian objects
  • Output data types:

    • CI energies
    • CI vectors
    • Natural orbitals
    • Excited states

Interfaces & Ecosystem

  • TheoChem tools: FanPy, ModelHamiltonian
  • Integral sources: PySCF interface
  • NumPy/SciPy: Sparse linear algebra
  • Diagonalization: Davidson, Lanczos

Advanced Features

Selected CI:

  • Heat-bath selection
  • Importance truncation
  • Perturbative corrections
  • Convergence extrapolation

Spin Adaptation:

  • Sz eigenfunctions
  • S² eigenfunctions
  • Genealogical coupling
  • CSF basis

Sparse Methods:

  • On-the-fly generation
  • Compressed storage
  • Efficient contractions
  • Large CI spaces

Performance Characteristics

  • Speed: Efficient determinant handling
  • Accuracy: Variational CI accuracy
  • System size: Small to medium (FCI), larger (sCI)
  • Memory: Sparse storage efficient
  • Parallelization: Potential for parallel

Computational Cost

  • FCI: Factorial scaling (small systems)
  • CISD: O(N^6) manageable
  • Selected CI: System dependent
  • Typical: Seconds to hours

Limitations & Known Constraints

  • FCI scaling: Exponential, limits system size
  • Size consistency: Not inherent in truncated CI
  • Production: Research focus
  • Documentation: Academic oriented

Comparison with Other Codes

  • vs MOLPRO CI: PyCI more flexible, MOLPRO faster
  • vs PySCF CI: Similar, different ecosystems
  • vs Arrow/DICE: Different sCI algorithms
  • Unique strength: TheoChem integration, flexibility

Application Areas

Benchmarks:

  • FCI reference energies
  • Correlation energy studies
  • Method validation

Strong Correlation:

  • Multi-reference systems
  • Bond breaking
  • Transition metals

Method Development:

  • CI algorithm testing
  • Selection scheme development
  • Hybrid methods

Best Practices

Calculation Setup:

  • Appropriate active space
  • Selection thresholds
  • Convergence criteria
  • State averaging for excited states

Efficiency:

  • Start with smaller CI
  • Increase systematically
  • Use selected CI for larger systems
  • Monitor memory usage

Community and Support

  • Open-source GPL v3
  • TheoChem group (McMaster University)
  • Academic publications
  • GitHub for issues
  • Growing user base

Verification & Sources

Primary sources:

  1. GitHub: https://github.com/theochem/pyci
  2. Ayers group publications
  3. CI methodology papers
  4. TheoChem documentation

Confidence: VERIFIED

  • Source code: OPEN (GitHub, GPL v3)
  • Documentation: ReadTheDocs
  • Academic group: TheoChem
  • Active development: Yes

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