GauNEGF

Note: This package is currently in testing and uses the Gaussian Development Version and Gaussian Python Interface, both of which are not publicly available. Please check the Gaussian website for updates on the forthcoming releases.

A Python package for performing Non-Equilibrium Green's Function (NEGF) calculations integrated with Gaussian quantum chemistry software.

Documentation: https://wliverno.github.io/GauNEGF/

This package builds upon the foundation laid by the open-source ANT.Gaussian package. The Bethe lattice implementation and numerical quadrature methods are adapted from ANT.Gaussian's Fortran implementation (Palacios et al., 2002; Jacob & Palacios, 2011).

The package incorporates methods from several key works in the field: - NEGF-DFT integration based on Damle et al. (2002) - SCF convergence acceleration using Pulay's method (1980) as implemented in ANT.Gaussian - Spin-dependent transport calculations inspired by Zöllner et al. (2020)

Note: This package requires a licensed copy of Gaussian quantum chemistry software to run. Gaussian is a registered trademark of Gaussian, Inc.

Features

• Integration with Gaussian quantum chemistry package
• Self-consistent field (SCF) calculations with NEGF formalism
• Surface Green's function calculations using Bethe lattice approach
• Transport calculations for molecular junctions
• Support for spin-polarized calculations
• Parallel processing capabilities

Requirements

Required Software

• gauopen 3.0.0 Python interface for Gaussian

Python Dependencies

• numpy
• scipy
• matplotlib

Installation

```bash

Clone the repository

git clone git@github.com:wliverno/GauNEGF.git cd GauNEGF

Install using pip

pip install -e . ```

Quick Start

```python from gauNEGF import scf, transport

Initialize NEGF calculation

negf = scf.NEGF("molecule", basis="lanl2dz", func="b3pw91")

Set contacts - left contact on atom 1, right contact on atom 2

negf.setContacts([1], [2])

Set voltage bias

negf.setVoltage(0.1) # 0.1V bias

Run SCF calculation

negf.SCF(1e-3, 0.02) # convergence @ 1e-3, damping=0.02

Calculate current

I = transport.quickCurrent(negf.F, negf.S, negf.sig1, negf.sig2, negf.fermi, negf.qV)

```

Documentation

Full API documentation with examples has been compiled and deployed with Github Pages: https://wliverno.github.io/GauNEGF/

For usage examples, see the files in the examples/ directory.

Citation

If you use GaussianNEGF in your research, please cite:

bibtex @software{gauNEGF2025 author = {Livernois, William}, license = {MIT}, month = April, year = {2025}, title = {{GauNEGF}}, url = {https://github.com/wliverno/GauNEGF}, version = {v0.1.0-alpha}, doi = {10.5281/zenodo.15178667}, url = {https://doi.org/10.5281/zenodo.15178667} }

Acknowledgments

• Prof. M. P. Anantram (University of Washington) - Debugging and testing underlying NEGF framework and quantum transport physics
• Dr. Mike Frisch (Gaussian Inc.) - Debugging and patching issues within Gaussian, theory and implementation for non-collinear spin treatment
• Prof. Juan José Palacios (Universidad Autónoma de Madrid) - Theory and implementation for Fermi energy calculation and energy-dependent contacts
• Alex Bernson - Technical editing and AI meta-framework development for documentation

References

1. Damle, P., Ghosh, A. W., & Datta, S. (2002). First-principles analysis of molecular conduction using quantum chemistry software. Chemical Physics, 281(2-3), 171-187. https://doi.org/10.1016/S0301-0104(02)00496-2
2. Jacob, D., & Palacios, J. J. (2011). Critical comparison of electrode models in density functional theory based quantum transport calculations. The Journal of Chemical Physics, 134(4), 044118. https://doi.org/10.1063/1.3526044
3. Palacios, J. J., Pérez-Jiménez, A. J., Louis, E., SanFabián, E., & Vergés, J. A. (2002). First-principles approach to electrical transport in atomic-scale nanostructures. Physical Review B, 66(3), 035322. https://doi.org/10.1103/PhysRevB.66.035322
4. Pulay, P. (1980). Convergence acceleration of iterative sequences. the case of scf iteration. Chemical Physics Letters, 73(2), 393-398. https://doi.org/10.1016/0009-2614(80)80396-4
5. Zöllner, M. S., Varela, S., Medina, E., Mujica, V., & Herrmann, C. (2020). Insight into the Origin of Chiral-Induced Spin Selectivity from a Symmetry Analysis of Electronic Transmission. Journal of Chemical Theory and Computation, 16(5), 2914-2929. https://doi.org/10.1021/acs.jctc.9b01078

License

This project is licensed under the MIT License - see the LICENSE file for details.