genheas

genheas (Generate High Entropy Alloys Structures) is a neural evolution structures (NESs) generation methodology combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs).

Support and Documentation

see docs for documentation, reporting bugs, and getting support.

Installation

• Install poetry (https://github.com/python-poetry/poetry)

  • osx / linux / bashonwindows install instructions

    bash recommended $ curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python -

    other possibility $ pip install poetry

  • windows powershell install instructions

    bash recommended $ (Invoke-WebRequest -Uri https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py -UseBasicParsing).Content | python -

other possibility

$ pip install poetry

• Once Poetry is installed you can execute the following:

bash

$ poetry --version

$ poetry self update

• Clone the repo

bash

$ git clone https://github.com/CLEANit/genheas

$ cd genheas

• Create a Virtual Environment

  By default, Poetry create virtual environment in $HOME/.poetry/env or $HOME/.cache/pypoetry/virtualenvs for cahcing/sharing purpose

  - to install install dependencies to current python interpreter/virtualenv .. sourcecode:: bash

  $genheas poetry config virtualenvs.create false --local

  - create virtual environment in default location .. sourcecode:: bash

  $genheas poetry config virtualenvs.create

  - create virtual environment in th root directory of a Python project .. sourcecode:: bash

  $genheas poetry config virtualenvs.in-project true

  - To change or otherwise add a new configuration setting, .. sourcecode:: bash

  $genheas poetry config virtualenvs.path /path/to/cache/directory/virtualenvs

- install the packages .. sourcecode:: bash

$genheas poetry install

$genheas poetry check

$genheas poetry run pytest

$genheas poetry build

• Listing the current configuration

  bash

  $genheas poetry config --list

  which will give you something similar to this

  bash

  cache-dir = "/path/to/cache/directory" virtualenvs.create = true virtualenvs.in-project = null virtualenvs.path = "{cache-dir}/virtualenvs" # /path/to/cache/directory/virtualenvs

• Show Information of the Vitual Environment

  bash

  poetry env info

  Virtualenv Python: 3.8.5 Implementation: CPython Path: "/path/to/cache/poetry virtual environment" Valid: True

  System Platform: linux OS: posix Python: /home/ctetsass/anaconda3

• Activate Virtual Environment

bash

$genheas poetry shell

Usage

Here is the schematic of the workflow to generate HEAs structures:

• train.y

  use small cell to train the model

• generate.py

  use the trained model to general large cell

• Therefore, it should looks like:

  1- Train the model and generate structure

  • Edit the configuration file both training and generation part

  - copy the configuration file to the "root_dir" and edit .. sourcecode:: bash $genheas/ cp genheas/parameters_template.yml rootdir/parameters.yml $root_dir vi parameters.yml

  - running from <workdir> .. sourcecode:: bash

  $ cd workdir after the activation of the virtual enviroment $workdir python main.py rootdir

  - running in the <genheas/genheas> directory .. sourcecode:: bash

  $genheas/genheas poetry run python main.py rootdir

  2- Only train a model

  • Edit the configuration file : training part

  bash

  $root_dir vi parameters.yml

  - run .. sourcecode:: bash

  $workdir python train.py rootdir

  3 - Using a pre-trained model to generate cell

  • Edit the configuration file : generation part

  bash

  $root_dir vi parameters.yml

  - run .. sourcecode:: bash

  $workdir python generate.py root_dir

Developing and Contributing

See contributing.md for guidelines on running tests, adding features, coding style, and updating documentation when developing in or contributing to genheas

Authors

Conrard Tetsassi