https://github.com/kousuke-nakano/turbogenius
Python wrappers for TurboRVB
https://github.com/kousuke-nakano/turbogenius
ab-initio-simulations electronic-structure python quantum-monte-carlo turborvb
Last synced: 6 months ago
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Python wrappers for TurboRVB
- Host: GitHub
- URL: https://github.com/kousuke-nakano/turbogenius
- Owner: kousuke-nakano
- License: bsd-3-clause
- Created: 2022-09-02T11:29:22.000Z (almost 4 years ago)
- Default Branch: devel
- Last Pushed: 2025-12-02T07:39:43.000Z (7 months ago)
- Last Synced: 2025-12-05T02:20:23.912Z (7 months ago)
- Topics: ab-initio-simulations, electronic-structure, python, quantum-monte-carlo, turborvb
- Language: Python
- Homepage: https://turborvb.sissa.it
- Size: 7.05 MB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# TurboGenius
`TurboGenius` is an advanced python wrappers for the SISSA ab-initio quantum Monte Carlo code, `TurboRVB` and also provides useful command-line tools.
   
`TurboRVB` software family is now composed of the 4 layered packages:
- `TurboWorkflows` (Workflows for realizing QMC high-throughput calculations)
- `TurboGenius` (Advanced python wrappers and command-line tools)
- `pyturbo` (Python-Fortran90 wrappers)
- `TurboRVB` (Quantum Monte Carlo kernel)
`TurboGenius` is the third layer package, which also contains the second layer package `pyturbo` inside as a submodule.
# Beta version
This is a **beta** version!!!! Contact the developers whenever you find bugs. Any suggestion is also welcome!
# Features of `turbogenius`
One can manage any job of `TurboRVB` on python scripts, or on your terminal using the provided command line tool `turbogenius`.
For python users, several one-to-one corresponding python modules (classes) are provided, i.e., `makefort10.x` -> `Makefort10_genius` class in `makefort10_genius.py`, `turborvb.x` -> `vmc_genius` class in `vmc_genius.py`. `TurboGenius` is designed as a higher layer package that provide several complicated procedures and functions such as fully automatic workflows. `Turbo-Genius` is implemented based on the lower layer packages `pyturbo` and `TurboRVB`. You can see several examples of `TurboGenius` scripts in the `tests` directory. You can also see several simple workflows using `TurboGenius` in the `tests` directory.
# Quick use of `turbogenius`
Installing from source
git clone https://github.com/kousuke-nakano/turbogenius.git
cd turbogenius
pip install -e . or pip install .
Lauching the command line tool. You can easily see what commands are implemented in ``TurboGenius``.
% turbogenius --help
Usage: turbogenius [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
convertfort10 convertfort10_genius
convertfort10mol convertfort10mol_genius
convertpfaff readforward_genius
convertwf convert wavefunction
correlated-sampling correlated_sampling_genius
lrdmc lrdmc_genius
lrdmcopt lrdmc_genius
makefort10 makefort10_genius
prep prep_genius
vmc vmc_genius
vmcopt vmcopt_genius
You can also see what options are implemented for each command, e.g., vmcopt
% turbogenius vmcopt --help
Usage: turbogenius vmcopt [OPTIONS]
Options:
-post Postprocess
-r Run a program
-g Generate an input file
-vmcoptsteps INTEGER Specify vmcoptsteps
-optwarmup INTEGER Specify optwarmupsteps
-steps INTEGER Specify steps per one iteration
-bin INTEGER Specify bin_block
-warmup INTEGER Specify warmupblocks
-nw INTEGER Specify num_walkers
-maxtime INTEGER Specify maxtime
-optimizer TEXT Specify optimizer, sr or lr
-learn FLOAT Specify learning_rate
-reg FLOAT Specify regularization
-opt_onebody flag for opt_onebody
-opt_twobody flag for opt_twobody
-opt_det_mat flag for opt_det_mat
-opt_jas_mat flag for opt_jas_mat
-opt_det_basis_exp flag for opt_det_basis_exp
-opt_jas_basis_exp flag for opt_jas_basis_exp
-opt_det_basis_coeff flag for opt_det_basis_coeff
-opt_jas_basis_coeff flag for opt_jas_basis_coeff
-twist flag for twist_average
-kpts INTEGER... kpts, Specify Monkhorst-Pack grids and shifts, [nkx,nky,nkz,kx,ky,kz]
-plot flag for plotting graph
-log TEXT logger level, DEBUG, INFO, ERROR
--help Show this message and exit.
# Features of `pyturbo`
One can manage any job of `TurboRVB` on python scripts. There are one-to-one corresponding python modules (classes), i.e., `makefort10.x` -> `Makefort10` class in `makefort10.py`, `convertfort10.x` -> `Convertfort10mol` class in `convertfort10mol.py`. `pyturbo` is designed as a lower layer package such that the modules can be used as **components** of higher-level packages. Indeed, the classes are implemented as simple but flexible as possible. Other complicated methods and modules such as fully automatic workflows should be provided at the higher-level packages such as `TurboGenius`. You can see several examples of `pyturbo` scripts in the `tests` directory.
# Installation
- `git clone this repository`
- `cd turbogenius`
- `pip install -e .` or `pip install .`
# Examples
Examples are in the `example` directory.
# Documentation for users
You can readily understand how to use `pyturbo` and `turbogenius` by looking at the sample python scripts in the `example` directory.
You can also see our tutorials [https://github.com/kousuke-nakano/turbotutorials].
# Documentation for deveopers
There is a Read the Docs in the `docs` directory, but still in progress.
You can generate a html file using `sphinx`. Go to the `docs` directory,
and type `make html`. The document is generated in `docs/_build/html`.
`index.html` is the main page.
# Reference
K. Nakano et al., [TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods](https://doi.org/10.1063/5.0179003), *J. Chem. Phys.* 159, 224801 (2023).
# How to contribute
Please do not directly push your changes to `main` and `devel` branch.
Please create a pull request on GitHub from your forked repository or a new branch (e.g. `devel-#1`).
# How to check the version info.
# Confirm the version number via `setuptools-scm`
python -m setuptools_scm
e.g., 1.1.4.dev28+gceef293.d20221123 -> = v1.1.4 or v1.1.4-alpha(for pre-release)