ccpy

ccpy is a Python-based coupled-cluster package developed by the Piecuch group at Michigan State University. It implements a variety of ground and excited-state coupled-cluster methods using a hybrid Python-Fortran approach for computatio…

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Overview

ccpy is a Python-based coupled-cluster package developed by the Piecuch group at Michigan State University. It implements a variety of ground and excited-state coupled-cluster methods using a hybrid Python-Fortran approach for computational efficiency.

Reference Papers

Reference papers are not yet linked for this code.

Full Documentation

Official Resources

  • Homepage: https://github.com/piecuch-group/ccpy
  • Documentation: In repository
  • Source Repository: https://github.com/piecuch-group/ccpy
  • License: GNU General Public License v3.0

Overview

ccpy is a Python-based coupled-cluster package developed by the Piecuch group at Michigan State University. It implements a variety of ground and excited-state coupled-cluster methods using a hybrid Python-Fortran approach for computational efficiency.

Scientific domain: Coupled-cluster theory, excited states, post-HF methods
Target user community: Researchers applying advanced coupled-cluster methods

Theoretical Methods

  • CCD, CCSD, CCSDT, CCSDTQ
  • CCSD(T) and other perturbative triples
  • Completely Renormalized CC (CR-CC)
  • Equation-of-Motion CC (EOM-CC)
  • Left eigenstate CC
  • Active-space CC methods
  • Similarity-transformed EOM

Capabilities (CRITICAL)

  • Ground-state coupled cluster
  • Excited-state EOM-CC
  • Left eigenstates for properties
  • Perturbative corrections
  • CR-CC methods (CR-CC(2,3))
  • Active-space extensions
  • Hybrid Python/Fortran
  • Efficient contractions

Key Strengths

Method Coverage:

  • Multiple CC approximations
  • Perturbative corrections
  • Renormalized methods
  • Excited states

Piecuch Methods:

  • CR-CC(2,3)
  • DEA/DIP-EOMCC
  • Active-space variants
  • Size-extensive corrections

Implementation:

  • Python front-end
  • Fortran performance
  • Modular design
  • Extensibility

Research Focus:

  • Method development
  • New approximations
  • Benchmark calculations

Inputs & Outputs

  • Input formats:

    • Molecular integrals
    • Python scripts
  • Output data types:

    • Energies
    • Amplitudes
    • Excitation energies
    • Transition properties

Interfaces & Ecosystem

  • PySCF: Integral interface
  • NumPy: Array operations
  • Fortran: Computation kernels

Advanced Features

CR-CC Methods:

  • Non-iterative corrections
  • Size-extensivity
  • Multi-reference character
  • Bond breaking

EOM-CC Variants:

  • EOM-CCSD
  • EOM-CCSDT
  • IP/EA/EE variants
  • Transition moments

Active Space:

  • CCSDt
  • CC(t;3)
  • Reduced scaling
  • Large systems

Performance Characteristics

  • Speed: Fortran-accelerated
  • Accuracy: High-level methods
  • System size: Medium molecules
  • Parallelization: OpenMP

Computational Cost

  • CCSD(T): O(N^7) perturbative
  • EOM-CCSD: O(N^6) per state
  • CR-CC: Additional corrections
  • Typical: Moderate molecules

Limitations & Known Constraints

  • Documentation: Research-focused
  • Large systems: Standard CC limitations
  • User interface: Requires expertise
  • Community: Research group centered

Comparison with Other Codes

  • vs CFOUR: Both high-level CC; different methods
  • vs MRCC: Both advanced CC
  • vs ccq: ccpy more methods, Piecuch focus
  • Unique strength: CR-CC, Piecuch group methods

Application Areas

Excited States:

  • EOM-CC calculations
  • Transition properties
  • Multi-state problems

Strong Correlation:

  • CR-CC for bond breaking
  • Active-space methods
  • Challenging systems

Benchmarks:

  • Reference calculations
  • Method validation
  • New developments

Verification & Sources

Primary sources:

  1. GitHub: https://github.com/piecuch-group/ccpy
  2. Piecuch group publications
  3. Michigan State University

Confidence: VERIFIED

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

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