Official Resources
- Source Repository: https://github.com/StxGuy/GreenCheetah
- Documentation: Included in repository
- License: Open source
Overview
GreenCheetah is a Non-Equilibrium Green's Function (NEGF) approach for quantum transport implemented in Fortran and C++/Armadillo. It provides efficient computation of quantum transport properties in nanoscale devices using the NEGF formalism.
Scientific domain: NEGF quantum transport, device simulation
Target user community: Researchers simulating quantum transport in nanoscale devices using NEGF
Theoretical Methods
- Non-equilibrium Green's function (NEGF)
- Tight-binding Hamiltonians
- Self-energy calculation
- Landauer-Büttiker formalism
- Recursive Green's function algorithm
- Transmission function calculation
Capabilities (CRITICAL)
- NEGF quantum transport calculation
- Transmission function
- Conductance calculation
- Tight-binding model transport
- Recursive Green's function algorithm
- Fortran and C++ implementations
- Efficient sparse matrix operations
Sources: GitHub repository
Key Strengths
Performance:
- Fortran/C++ implementation
- Armadillo linear algebra
- Sparse matrix optimization
- Recursive algorithm efficiency
NEGF Framework:
- Full Green's function calculation
- Self-energy for leads
- Open boundary conditions
- Bias-dependent transport
Inputs & Outputs
-
Input formats:
- Hamiltonian matrix files
- Lead parameters
- Transport configuration
-
Output data types:
- Transmission vs energy
- Conductance
- Local density of states
- Current density
Interfaces & Ecosystem
- Fortran/C++: Core computation
- Armadillo: Linear algebra library
- Python: Possible scripting interface
Performance Characteristics
- Speed: Fast (compiled code)
- Accuracy: Depends on Hamiltonian
- System size: Thousands of orbitals
- Memory: Moderate
Computational Cost
- Transmission: Seconds to minutes
- Typical: Efficient
Limitations & Known Constraints
- Tight-binding only: No DFT integration
- Limited documentation: Research code
- Small community: Research group code
- No GUI: Command-line only
Comparison with Other Codes
- vs Gollum: GreenCheetah is Fortran/C++, Gollum is more feature-rich
- vs Jiezi: GreenCheetah is compiled, Jiezi is Python with self-consistent Poisson
- vs Nanodcal: GreenCheetah is open source TB, Nanodcal is commercial LCAO
- Unique strength: Efficient Fortran/C++ NEGF implementation with Armadillo, recursive Green's function
Application Areas
Nanoscale Transport:
- Nanowire conductance
- Molecular junctions
- Quantum dot transport
- 2D material devices
Method Development:
- NEGF algorithm testing
- Recursive GF benchmarks
- Sparse matrix optimization
- Transport method comparison
Best Practices
Hamiltonian Setup:
- Use well-parameterized TB models
- Include sufficient lead layers
- Check convergence of self-energies
- Validate against analytical models
Performance:
- Use sparse matrix mode
- Optimize memory layout
- Profile hot paths
- Compare Fortran vs C++ paths
Community and Support
- Open source on GitHub
- Research code
- Limited documentation
- Example calculations provided
Verification & Sources
Primary sources:
- GitHub: https://github.com/StxGuy/GreenCheetah
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Documentation: Included in repository
- Active development: Research code
- Specialized strength: Efficient Fortran/C++ NEGF implementation with Armadillo, recursive Green's function