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

DPNEGF runs inside the DeePTB virtual environment. We use UV as the package manager.

• Requirements

  • Git
  • Python 3.9 to 3.12 (UV can auto-install if needed)
  • DeePTB ≥ 2.1.1

• Step 1: Install UV (if not already installed)

  # On macOS and Linux
  curl -LsSf https://astral.sh/uv/install.sh | sh
  
  # Or using pip
  pip install uv
  
  # On Windows (PowerShell)
  powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

• Step 2: Install DeePTB

  git clone https://github.com/deepmodeling/DeePTB.git
  cd DeePTB
  uv sync  # Creates .venv and installs DeePTB with all dependencies

  For GPU support, see DeePTB README.

• Step 3: Add DPNEGF to the DeePTB environment

  # Clone the DPNEGF repository (you can clone it anywhere but not inside the DeePTB directory for clearity)
  git clone https://github.com/deepmodeling/dpnegf.git
  
  # Still inside the DeePTB directory
  uv add /path/to/dpnegf

  Replace /path/to/dpnegf with the actual path to your cloned DPNEGF repository.

• Run DPNEGF

  # UV automatically activates the environment
  uv run dpnegf --help
  
  # Or activate manually
  source .venv/bin/activate  # On Unix/macOS
  .venv\Scripts\activate     # On Windows
  dpnegf --help

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, 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.