PAUXY

NOTE: Pauxy is deprecated in favour of ipie. This repository is no longer maintained.

If you found pauxy to be helpful please cite ipie:

@article{malone2022ipie,: title={ipie: A Python-Based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs}, author={Malone, Fionn D and Mahajan, Ankit and Spencer, James S and Lee, Joonho}, journal={Journal of Chemical Theory and Computation}, volume={19}, number={1}, pages={109--121}, year={2022}, publisher={ACS Publications}

}

PAUXY is a collection of Python implementations of AUXilliarY field quantum Monte Carlo algorithms with a focus on simplicity rather than speed.

Features

PAUXY can currently:

estimate ground state properties of real (ab-initio) and model (Hubbard + UEG) systems.
perform phaseless and constrained path AFQMC.
calculate expectation values and correlation functions using back propagation.
calculate imaginary time correlation functions.
perform simple data analysis.

Clone the repository

$ git clone https://github.com/pauxy-qmc/pauxy.git

and run the following in the top-level pauxy directory

$ pip install -r requirements.txt
$ python setup.py build_ext --inplace
$ python setup.py install

You may also need to set your PYTHONPATH appropriately.

Minimum versions are listed in the requirements.txt. To run the tests you will need pytest. To perform error analysis you will also need pyblock.

Pauxy contains unit tests and some longer driver tests that can be run using pytest by running:

$ pytest -v

in the base of the repo. Some longer parallel tests are also run through the CI. See travis.yml for more details.

Documentation and tutorials are available at readthedocs.