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, 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 -> <next-version> = v1.1.4 or v1.1.4-alpha(for pre-release)