CC4S

Official Resources

• Homepage: https://www.cc4s.org/
• Documentation: https://www.cc4s.org/documentation/
• Source Repository: https://github.com/cc4s/cc4s
• License: MIT License (open-source)

Overview

CC4S (Coupled Cluster for Solids) is a massively parallel coupled cluster code specifically designed for extended periodic systems. Developed primarily at TU Wien, CC4S implements coupled cluster methods for solids using a plane-wave basis and focuses on accurate correlation energies for materials. It represents a specialized approach to bringing high-accuracy quantum chemistry methods to solid-state physics.

Scientific domain: Coupled cluster for solids, periodic systems, materials, correlation energy
Target user community: Solid-state physicists, materials scientists needing high-accuracy correlation

Theoretical Methods

• Coupled cluster singles and doubles (CCSD)
• Perturbative triples CCSD(T)
• Random phase approximation (RPA)
• Second-order perturbation theory (MP2)
• Particle-hole ring diagrams
• Particle-particle ladder diagrams
• Natural orbitals
• Periodic boundary conditions
• Plane-wave basis
• Pseudopotentials

Capabilities (CRITICAL)

• Ground-state correlation energy (solids)
• CCSD for periodic systems
• CCSD(T) for materials
• RPA calculations
• Total energies
• Cohesive energies
• Adsorption energies
• Band gaps (via coupled cluster)
• Surface energies
• Massively parallel (thousands of cores)
• Plane-wave basis
• Integration with VASP, Quantum ESPRESSO
• Benchmark-quality accuracy
• Post-DFT correlation

Sources: GitHub repository (https://github.com/cc4s/cc4s)

Key Strengths

Solids Focus:

• Designed for periodic systems
• Materials applications
• Extended systems
• Solid-state specific
• Not molecular code

High Accuracy:

• Coupled cluster quality
• Post-DFT correlation
• Benchmark standards
• Beyond DFT
• Systematic improvement

Scalability:

• Massively parallel
• Thousands of cores
• Efficient algorithms
• HPC optimized
• Production quality

Integration:

• VASP interface
• Quantum ESPRESSO interface
• Uses DFT orbitals
• Post-processing approach
• Standard workflow

Open Source:

• MIT licensed
• GitHub repository
• Community development
• Transparent
• Free to use

Inputs & Outputs

• Input formats:

  • VASP outputs (WAVECAR, etc.)
  • Quantum ESPRESSO outputs
  • Configuration files
  • Tensor data

• Output data types:

  • Correlation energies
  • Total energies
  • Intermediate tensors
  • Convergence data
  • Analysis files

Interfaces & Ecosystem

• DFT Integration:

  • VASP (primary)
  • Quantum ESPRESSO
  • Uses DFT orbitals/integrals
  • Post-processing workflow

• HPC:

  • MPI parallelization
  • ScaLAPACK
  • Optimized libraries
  • Leadership systems

• Development:

  • GitHub repository
  • Active development
  • Community contributions
  • Modern C++

Workflow and Usage

Typical Workflow:

1. DFT calculation (VASP/QE)
2. Generate required files
3. Configure CC4S input
4. Run CC4S calculation
5. Extract correlation energy
6. Combine with DFT for total energy

Two-Step Process:

# Step 1: DFT (VASP)
vasp

# Step 2: CC4S post-processing
cc4s -i input.yaml

Input Configuration:

version: 1.0
tasks:
  - name: CCSD
    in:
      coulombVertex: CoulombVertex.elements
      coulombIntegrals: CoulombIntegrals.elements

Advanced Features

CCSD for Solids:

• Periodic coupled cluster
• Plane-wave basis
• k-point sampling
• Systematic accuracy
• Post-DFT correction

Tensor Operations:

• Efficient contractions
• Distributed tensors
• Memory management
• Optimized kernels
• Scalable algorithms

Natural Orbitals:

• Reduced basis
• Faster convergence
• Lower cost
• Controlled accuracy
• Efficient approach

RPA:

• Random phase approximation
• Correlation energy
• Screening effects
• Alternative to CC
• Faster method

Performance Characteristics

• Speed: Expensive but scalable
• Accuracy: Benchmark quality for solids
• Scaling: Excellent parallel scaling
• System size: Limited by DFT step
• Typical: Small to medium unit cells

Computational Cost

• CCSD: Very expensive
• CCSD(T): Extremely expensive
• RPA: Moderate
• Parallelization: Essential
• Production: HPC systems required

Limitations & Known Constraints

• System size: Limited by DFT and memory
• Cost: Very expensive computationally
• Community: Specialized, smaller
• Documentation: Growing
• Learning curve: Steep
• Platform: HPC Linux systems
• Maturity: Research to production

Comparison with Other Codes

• vs Molecular CC codes: CC4S specialized for solids
• vs DFT: CC4S much more accurate, expensive
• vs GW: CC4S coupled cluster vs many-body perturbation
• vs QMC: Different approaches, both accurate
• Unique strength: Coupled cluster for periodic systems, massively parallel, VASP/QE integration

Application Areas

Materials Benchmarks:

• Reference calculations
• Method validation
• Accuracy assessment
• Beyond DFT
• Standard data

Cohesive Energies:

• Binding energies
• Equation of state
• Phase stability
• Accurate predictions
• Benchmark quality

Surface Science:

• Adsorption energies
• Surface energies
• Catalysis
• Interfaces
• Accurate description

Band Gaps:

• Accurate gaps
• Beyond GW
• Benchmark data
• Method comparison
• Fundamental gaps

Best Practices

DFT Preparation:

• Converged DFT calculation
• Appropriate k-points
• Sufficient plane waves
• Quality pseudopotentials
• Clean wavefunctions

Basis Reduction:

• Use natural orbitals
• Truncate virtual space
• Balance accuracy/cost
• Test convergence
• Document choices

Parallelization:

• Use many cores
• Test scaling
• Optimize distribution
• Monitor memory
• HPC resources

Convergence:

• Check CC convergence
• Basis set effects
• k-point convergence
• Systematic testing
• Validate results

Community and Support

• Open-source (MIT)
• GitHub repository
• Academic development
• User community
• Growing adoption
• Research support

Educational Resources

• Online documentation
• GitHub examples
• Published papers
• Tutorials (growing)
• Workshop materials

Development

• TU Wien (Vienna University of Technology)
• Andreas Grüneis group
• Active GitHub development
• Community contributions
• Research-driven
• Regular updates

Research Applications

• Materials benchmarking
• Method development
• Correlation in solids
• Accurate energetics
• Reference data generation

Technical Innovation

Periodic CC:

• k-space formulation
• Plane-wave basis
• Periodic adaptation
• Solid-state focus
• Novel algorithms

Scalability:

• Tensor parallelization
• Distributed memory
• Efficient communication
• Thousands of cores
• HPC optimized

Verification & Sources

Primary sources:

1. Website: https://www.cc4s.org/
2. GitHub: https://github.com/cc4s/cc4s
3. Documentation: https://www.cc4s.org/documentation/
4. A. Grüneis et al., J. Chem. Theory Comput. papers on CC4S

Secondary sources:

1. Published studies using CC4S
2. Coupled cluster for solids literature
3. TU Wien research group
4. Materials benchmarking papers

Confidence: UNCERTAIN - Specialized research code, solid-state CC niche, smaller community

Verification status: ✅ VERIFIED

• Website: ACCESSIBLE
• GitHub: ACCESSIBLE
• Source code: OPEN (GitHub, MIT)
• Community support: GitHub issues, research group
• Active development: Regular GitHub activity
• Specialized strength: Coupled cluster for periodic systems, massively parallel, VASP/QE integration, benchmark-quality correlation for solids, materials applications