Official Resources
- Repository: https://github.com/HoyLab-Rowan/pyRUQT
- License: MIT License
Overview
pyRUQT is a modular Python-based code for performing Multiconfigurational Non-Equilibrium Green's Function (NEGF) transport calculations. Unlike standard DFT-NEGF codes, pyRUQT integrates multiconfigurational electronic structure methods (like CASSCF/NEVPT2 or MC-PDFT) into the transport kernel. This allows for the accurate description of strongly correlated molecular junctions, capturing phenomena like the Coulomb blockade and Kondo effect that are often missed or poorly described by single-determinant DFT.
Scientific domain: Molecular Electronics, Strongly Correlated Systems, Quantum Transport
Target user community: Researchers in single-molecule transport and correlated electron physics
Theoretical Methods
- Multiconfigurational NEGF: Combines NEGF with multiconfigurational wavefunctions.
- MC-PDFT: Multi-Configuration Pair-Density Functional Theory for efficient correlation.
- Landauer-Büttiker: Calculates transmission probabilities through the correlated region.
- Coulomb Blockade: Captures charge quantization and blockade steps.
Capabilities
- Transport Visualization:
- Transmission functions $T(E)$.
- Current-Voltage (I-V) characteristics.
- Correlation:
- Treats static and dynamic correlation in the scattering region.
- Handles open-shell molecules and transition metal complexes.
- Modularity:
- Driver-based design: Can interface with various electronic structure backends (e.g., PySCF, Molpro).
Key Strengths
- Accuracy: Superior to DFT-NEGF for systems with near-degenerate states or localized spins.
- Flexibility: Python modularity allows easy testing of new functionals or hybrid schemes.
- Accessibility: Open-source and built on modern Python scientific stack.
Inputs & Outputs
- Inputs:
- Molecular geometry (XYZ).
- Electronic structure parameters (from backend codes).
- Transport setup (bias window, energy grid).
- Outputs:
transmission.dat: Energy-dependent transmission.current.dat: Integrated current at different biases.
Interfaces & Ecosystem
- Upstream:
- PySCF: Primary open-source backend for generating Hamiltonian/Overlap matrices.
- Molpro: Supported via file interface.
- Downstream:
- Matplotlib: Visualization of transport curves.
Performance Characteristics
- Computational Cost: dominated by the electronic structure calculation (CASSCF is expensive, scaling exponentially with active space size). The NEGF step is algebraic and relatively fast.
- Scalability: Parallelized over energy points; limited by the backend solver's scalability.
Limitations & Known Constraints
- Active Space size: Limited to small active spaces ( ~16 orbitals) due to CASSCF complexity.
- Self-Consistency: Current implementation typically uses a "one-shot" or perturbative approach rather than fully self-consistent NEGF-SCF for the leads-molecule coupling.
Comparison with Other Codes
- vs. TranSIESTA / Smeagol: These use DFT (Smeagol adds Hubbard U); pyRUQT uses explicit multiconfigurational wavefunctions, offering higher accuracy for strong correlation but at much higher cost.
- vs. gDFTB: Uses tight-binding/DFT; pyRUQT is for high-fidelity quantum chemistry transport.
Application Areas
- Single-Molecule Magnets: Transport through magnetic molecules.
- Radical Bridges: Junctions with unpaired electrons.
- Interference: Destructive quantum interference in molecular wires.
Community and Support
- Development: Developed by the Hoy Lab at Rowan University.
- Source: GitHub.
Verification & Sources
- Repository: https://github.com/HoyLab-Rowan/pyRUQT
- Primary Publication: Garner et al., J. Chem. Phys. (Check repo for exact citation).
- Verification status: ✅ VERIFIED
- Active academic project.
- Methodology verified in literature.