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)
2. DeePTB-SK: Nat Commun 15, 6772 (2024)
3. DeePTB-E3: ICLR 2025 Spotlight

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:

     git clone https://github.com/DeePTB-Lab/dpnegf.git
     

  2. Navigate to the root directory and install DPNEGF:

     cd dpnegf
     pip install .
     

Test code

To ensure the code is correctly installed, please run the unit tests first:

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, 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.