https://github.com/w2dynamics/w2dynamics

A continuous-time hybridization-expansion Monte Carlo code for calculating n-particle Green's functions of the Anderson impurity model and within dynamical mean-field theory.
https://github.com/w2dynamics/w2dynamics

monte-carlo-simulation python quantum-monte-carlo

Last synced: 6 months ago
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A continuous-time hybridization-expansion Monte Carlo code for calculating n-particle Green's functions of the Anderson impurity model and within dynamical mean-field theory.

• Host: GitHub
• URL: https://github.com/w2dynamics/w2dynamics
• Owner: w2dynamics
• License: gpl-3.0
• Created: 2018-01-11T13:38:49.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-09-12T13:39:10.000Z (about 1 year ago)
• Last Synced: 2024-11-01T13:34:03.970Z (11 months ago)
• Topics: monte-carlo-simulation, python, quantum-monte-carlo
• Language: Fortran
• Homepage:
• Size: 683 KB
• Stars: 50
• Watchers: 15
• Forks: 22
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION

Awesome Lists containing this project

README

          
w2dynamics - Wien/Wuerzburg strong coupling solver

==================================================

w2dynamics is a hybridization-expansion continuous-time quantum Monte Carlo

package, developed jointly in Wien and Würzburg. 

**In any published papers arising from the use of w2dynamics, please cite:**

   M. Wallerberger, A. Hausoel, P. Gunacker, A. Kowalski, N. Parragh, F. Goth, K. Held, and G. Sangiovanni,  

   Comput. Phys. Commun. 235, 2 (2019)  

     

   arXiv:1801.10209   

   When using additional codes in  conjunction with w2dynamics, do not forget to give credit to them as well.  

w2dynamics contains:

 - a multi-orbital quantum impurity solver for the Anderson impurity model

 - dynamical mean field theory self-consistency loop,

 - a maximum-entropy analytic continuation, as well as

 - coupling to density functional theory.

The w2dynamics package allows for calculating one- and two-particle quantities;

it includes worm and further novel sampling schemes. Details about its download,

installation, functioning and the relevant parameters are provided.

Maintainers and principal authors:

  - Markus Wallerberger

  - Andreas Hausoel

  - Patrik Gunacker

  - Alexander Kowalski

  - Nicolaus Parragh

  - Florian Goth

  - Karsten Held

  - Giorgio Sangiovanni

Installation

------------

Requirements:

  - [Python](https://www.python.org/) (>= 2.6)

  - Fortran 90 compiler, e.g. [GCC](https://gcc.gnu.org/)

  - C++11 compiler, e.g. [GCC](https://gcc.gnu.org/)

  - [CMake](https://cmake.org/) (>= 3.18)

  - [BLAS](https://www.netlib.org/blas/), ideally an optimized version as provided e.g. by [OpenBLAS](https://www.openblas.net/) or [Intel MKL](https://software.intel.com/mkl)

  - [LAPACK](https://www.netlib.org/lapack/), ideally an optimized version as provided e.g. by [OpenBLAS](https://www.openblas.net/) or [Intel MKL](https://software.intel.com/mkl)

Further dependencies (automatically installed if not found):

  - Python packages: [numpy](https://pypi.org/project/numpy/) >= 1.10, [scipy](https://pypi.org/project/scipy/) >= 0.10, [h5py](https://pypi.org/project/h5py/), [mpi4py](https://pypi.org/project/mpi4py/), [configobj](https://pypi.org/project/configobj/)

  - [NFFT3](https://www-user.tu-chemnitz.de/~potts/nfft/) (for automatic building, its dependency [FFTW3](http://www.fftw.org/) is required)

  - [HDF5](https://www.hdfgroup.org/solutions/hdf5) (as a dependency of h5py)

To get the code use `git`:

    $ git clone https://github.com/w2dynamics/w2dynamics.git

To build the code follow the `cmake` build model:

    $ mkdir build

    $ cd build

    $ cmake .. [FURTHER_FLAGS_GO_HERE]

    $ make

If CMake fails to find some native dependency automatically, command

line arguments of the form `-D_ROOT=/path/to/package/` can

usually be used to explicitly specify installation prefixes that

should be searched, for example `-DNFFT_ROOT=/path/to/nfft/` for

NFFT. For the Python packages, you should try to ensure that they can

be imported in the interpreter used by CMake. CMake can be instructed

to use a specific interpreter executable using

`-DPYTHON_EXECUTABLE=/path/to/python`. Consider especially that if a

Python 3 installation is found automatically, it will be preferred to

a Python 2 installation.

To run the unit tests (optional), run the following in the build directory:

    $ make test

To install the code (optional), run the following in the build directory:

    $ cmake .. -DCMAKE_INSTALL_PREFIX=/path/to/prefix

    $ make install

For example installation instructions for some operating systems and

computing clusters, you can also visit our wiki page on

[installation](https://github.com/w2dynamics/W2Dynamics/wiki/Installation).

Running the code

----------------

First, prepare a parameter file, which is usually named

`Parameters.in`. You can use the input files for the

[tutorials](https://github.com/w2dynamics/w2dynamics/wiki/Tutorials)

on our wiki as templates.

Next, run `DMFT.py` to use the self-consistency loop or `cthyb` if you

do a single-shot calculation. If you have installed the code (see

above), both executables should be placed in your path. If your

parameter file is not named `Parameters.in` or does not lie in your

current working directory, you have to specify it explicitly as a

command line argument. If you want to run your calculation using MPI

parallelization, use `mpiexec` or similar to execute the script.

    $ DMFT.py [Parameters.in]

    $ cthyb [Parameters.in]

    $ mpiexec -n 10 DMFT.py [Parameters.in]

    $ mpiexec -n 10 cthyb [Parameters.in]

The code will produce a file with a name like `RunIdentifier-Timestamp.hdf5`.

It is an archive of all quantities written by w2dynamics.  You can navigate this

file using any hdf5-compatible analysis tool, such as jupyter, matlab, etc.

For your convenience, we have also included the tool `hgrep`, which allows

quick analysis of the data:

    $ hgrep [options] (file|latest) quantity [[index] ...]

    $ hgrep latest siw 1 1 1 1

will print the self-energy on the Matsubara axis from the first

iteration for the first inequality, orbital and spin as tabular

data. Add option `-p` for automatic plotting or have a look at the man

page `hgrep.man` or the examples in our

[tutorials](https://github.com/w2dynamics/w2dynamics/wiki/Tutorials)

for details.

Files and directories

---------------------

  - `w2dyn/auxiliaries/`: auxiliary python routines (in/output, config files, etc.)

  - `w2dyn/dmft/`: python package for DMFT self-consistency loop

  - `w2dyn/maxent/`: Python wrapper for maximum entropy analytic continuation

  - `clusters/`: template submission scripts for different clusters

  - `cmake/`: cmake custom modules

  - `docs/`: documentation (github wiki)

  - `Postproc/`: postprocessing scripts

  - `preproc/`: preprocessing scripts

  - `src/`: compiled modules loaded from python

    - `ctqmc_fortran`: Fortran 90 continuous-time quantum Monte Carlo solver

    - `maxent`: maximum entropy analytic continuation solver

    - `mtrng`: Mersenne twister pseudorandom number generator

  - `testsuite/`: unit tests for the code

  - `cfg_converter.py`: small script converting old-style config files

  - `completions.sh`: file for bash completions

  - `cprun`: convenience script copying input files to different directory

  - `DMFT.py`: main entry point for DMFT self-consistency loop

  - `hgrep`: utility for extracting data from HDF5 file

  - `Maxent.py`: main entry point for maximum entropy code

  - `setup.py`: Python installation script

Citation

--------

If you use the w2dynamics package, please mention the following in the acknowledments:

The QMC simulations were carried out with the w2dynamics package available at https://github.com/w2dynamics/w2dynamics .

[Disclaimer](https://www.uni-wuerzburg.de/sonstiges/impressum/)

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