https://github.com/deepmodeling/dpnegf
A NEGF Python package compatible to DeePTB method for efficient quantum transport simulations
https://github.com/deepmodeling/dpnegf
Last synced: 2 months ago
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A NEGF Python package compatible to DeePTB method for efficient quantum transport simulations
- Host: GitHub
- URL: https://github.com/deepmodeling/dpnegf
- Owner: deepmodeling
- License: lgpl-3.0
- Created: 2025-12-22T01:42:12.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-01-19T09:02:16.000Z (2 months ago)
- Last Synced: 2026-01-19T16:50:41.443Z (2 months ago)
- Language: Python
- Homepage:
- Size: 3.37 MB
- Stars: 1
- Watchers: 0
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
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README
# DPNEGF
**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green’s Function (**NEGF**) method, establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy.
By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport properties in open-boundary systems with either environment-corrected **Slater-Koster TB Hamiltonian** or **linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian**.
For more details, see our papers:
1. [DPNEGF: npj Comput Mater 11, 375 (2025)](https://www.nature.com/articles/s41524-025-01853-6)
2. [DeePTB-SK: Nat Commun 15, 6772 (2024)](https://doi.org/10.1038/s41467-024-51006-4)
3. [DeePTB-E3: ICLR 2025 Spotlight](https://openreview.net/forum?id=kpq3IIjUD3)
## Installation
Installing **DPNEGF** is straightforward. We recommend using a virtual environment for dependency management.
- **Requirements**
- Git
- DeePTB(https://github.com/deepmodeling/DeePTB) ≥ 2.1.1
- **From Source**
1. Clone the repository:
```bash
git clone https://github.com/deepmodeling/dpnegf.git
```
2. Navigate to the root directory and install DPNEGF:
```bash
cd dpnegf
pip install .
```
## Test code
To ensure the code is correctly installed, please run the unit tests first:
```bash
pytest ./dpnegf/tests/
```
Be careful if not all tests pass!
## How to cite
The following references are required to be cited when using DPNEGF. Specifically:
- **For DPNEGF:**
J. Zou, Z. Zhouyin, D. Lin, Y. Huang, L. Zhang, S. Hou and Q. Gu, Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors, npj Comput Mater 11, 375 (2025).
- **For DeePTB-SK:**
Q. Gu, Z. Zhouyin, S. K. Pandey, P. Zhang, L. Zhang, and W. E, Deep Learning Tight-Binding Approach for Large-Scale Electronic Simulations at Finite Temperatures with Ab Initio Accuracy, Nat Commun 15, 6772 (2024).
- **For DeePTB-E3:**
Z. Zhouyin, Z. Gan, S. K. Pandey, L. Zhang, and Q. Gu, Learning Local Equivariant Representations for Quantum Operators, In The 13th International Conference on Learning Representations (ICLR) 2025.