References

• Kurashige, Yuki. “Matrix product state formulation of the multiconfiguration time-dependent Hartree theory.” The Journal of Chemical Physics 149.19 (2018): 194114.

  • Time evolution algorithm of MPS

  • Population dynamics

• Hino, Kentaro, and Yuki Kurashige. “Matrix Product State Formulation of the MCTDH Theory in Local Mode Representations for Anharmonic Potentials.” Journal of Chemical Theory and Computation 18.6 (2022): 3347-3356.

  • Spectroscopy

  • Anharmonic potential

  • Local mode vibration

• Hino, Kentaro, and Yuki Kurashige. “Encoding a Many-body Potential Energy Surface into a Grid-Based Matrix Product Operator.” Journal of Chemical Theory and Computation 20.9 (2024): 3839-3849.)

  • Grid-based MPO

  • DVR-MPS

  • nMR

• Beck, Michael H., et al. “The multiconfiguration time-dependent Hartree (MCTDH) method: a highly efficient algorithm for propagating wavepackets.” Physics reports 324.1 (2000): 1-105.

  • MCTDH paper

  • DVR is also written.

Installation

• The easiest way to install pytdscf is to use pip.

  Prepare Python 3.10 or later and execute;

  $ python -m venv pytdscf-env
  $ source pytdscf-env/bin/activate
  $ pip install git+https://github.com/QCLovers/PyTDSCF
  

• We recommend install pytdscf from source using uv

  $ git clone https://github.com/QCLovers/PyTDSCF.git
  $ cd PyTDSCF
  $ uv version
  uv 0.5.4 (c62c83c37 2024-11-20)
  $ uv sync --all-extras
  

  will install all dependencies including development tools. If you need only the runtime dependencies, you can use uv sync --no-dev.

  Then, you can execute pytdscf by

  $ uv run python xxx.py
  

  or

  $ souce .venv/bin/activate
  $ python
  >>> import pytdscf
  

  For jupyter notebook tutorials, you can use

  $ uv run jupyter lab
  

For GPU users

pytdscf works both on CPU and GPU. If you treat large-scale batch or model, we recommend using GPU. See also JAX’s GPU support.

1. Make sure the latest NVIDIA driver is installed.

   $ /usr/local/cuda/bin/nvcc -V
   nvcc: NVIDIA (R) Cuda compiler driver
   Copyright (c) 2005-2024 NVIDIA Corporation
   Built on Wed_Apr_17_19:19:55_PDT_2024
   Cuda compilation tools, release 12.5, V12.5.40
   Build cuda_12.5.r12.5/compiler.34177558_0
   

2. Install GPU-supported JAX in your virtual envirionment.

   $ uv pip install -U "jax[cuda12]"
   $ uv run python -c "import jax; print(jax.default_backend())"
   'gpu'
   

Testing

$ cd tests/build
$ uv run pytest ..

For developers

You should install pre-commit hooks including ruff formatting and linting, mypy type checking, pytest testing, and so on.

$ uv run pre-commit install
$ git add .
$ uv run pre-commit

Before push, you must fix problems!!

Please feel free to give us feedback or pull requests.

How to run

See quick-start example in documentation or test directory.