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:
- DPNEGF: npj Comput Mater 11, 375 (2025)
- DeePTB-SK: Nat Commun 15, 6772 (2024)
- DeePTB-E3: ICLR 2025 Spotlight
Installing DPNEGF is straightforward. We recommend using a virtual environment for dependency management.
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Requirements
- Git
- DeePTB(https://github.com/deepmodeling/DeePTB) ≥ 2.1.1
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From Source
- Clone the repository:
git clone https://github.com/deepmodeling/dpnegf.git
- Navigate to the root directory and install DPNEGF:
cd dpnegf pip install .
- Clone the repository:
To ensure the code is correctly installed, please run the unit tests first:
pytest ./dpnegf/tests/Be careful if not all tests pass!
The following references are required to be cited when using DPNEGF. Specifically:
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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).
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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).
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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.