Metadata-Version: 2.1
Name: modnet
Version: 0.2.1
Summary: MODNet, the Material Optimal Descriptor Network for materials properties prediction. 
Home-page: https://github.com/ppdebreuck/modnet
Author: Pierre-Paul De Breuck
Author-email: pierre-paul.debreuck@uclouvain.be
License: UNKNOWN
Project-URL: GitHub, https://github.com/ppdebreuck/modnet
Project-URL: Documentation, https://modnet.readthedocs.io
Description: # MODNet: Material Optimal Descriptor Network
        
        [![arXiv](https://img.shields.io/badge/arXiv-2004.14766-brightgreen)](https://arxiv.org/abs/2004.14766) [![Build Status](https://img.shields.io/github/actions/workflow/status/ppdebreuck/modnet/ci.yml?logo=github&branch=main)](https://github.com/ppdebreuck/modnet/actions?query=branch%3Amaster+) [![Read the Docs](https://img.shields.io/readthedocs/modnet)](https://modnet.readthedocs.io/en/latest/)
        
        <a name="introduction"></a>
        ## Introduction
        This repository contains the Python (3.8+) package implementing the Material Optimal Descriptor Network (MODNet).
        It is a supervised machine learning framework for **learning material properties** from
        either the **composition** or  **crystal structure**. The framework is well suited for **limited datasets**
        and can be used for learning *multiple* properties together by using **joint learning**.
        
        MODNet appears on the [MatBench leaderboard](https://matbench.materialsproject.org/). As of 11/11/2021, MODNet provides the best performance of all submitted models on 7 out of 13 tasks.
        
        This repository also contains two [pretrained models](#pretrained) that can be used for predicting
        the refractive index and vibrational thermodynamics from any crystal structure.
        
        See the MODNet papers and repositories below for more details:
        
        - De Breuck *et al.*, "Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet." *npj Comput Mater* **7**, 83 (2021). [10.1038/s41524-021-00552-2](https://doi.org/10.1038/s41524-021-00552-2) (preprint: [arXiv:2004.14766](https://arxiv.org/abs/2004.14766)).
        - De Breuck *et al.*, "Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet." *J. Phys.: Condens. Matter* **33** 404002,  (2021), [10.1088/1361-648X/ac1280](https://doi.org/10.1088/1361-648X/ac1280) (preprint: [arXiv:2102.02263](https://arxiv.org/abs/2102.02263)).
        - MatBench benchmarking data repository: [modl-uclouvain/modnet-matbench](https://github.com/modl-uclouvain/modnet-matbench).
        
        
        
        <p align='center'>
        <img src="img/MODNet_schematic.PNG" alt="MODNet schematic" />
        </p>
        <div align='center'>
        <strong>Figure 1. Schematic representation of the MODNet.</strong>
        </div>
        
        
        <a name="install"></a>
        ## How to install
        
        First, create a virtual environment (e.g., named modnet) with Python (3.8+) using
        your favourite environment manager (the following instructions use
        [conda](https://docs.conda.io/)):
        
        ```shell
        conda create -n modnet python=3.9
        ```
        
        Activate the environment:
        
        ```shell
        conda activate modnet
        ```
        
        Finally, install MODNet from PyPI with `pip`:
        
        ```shell
        pip install modnet
        ```
        
        >**Warning**
        >We strongly recommend pinning your Python environment when using MODNet
        >across multiple machines, or multiple MODNet versions, as changes to the
        >dependencies and sub-dependencies can lead to different values for particular
        >features.
        >
        >This can be achieved with `conda export` or `pip freeze`.
        
        For development (or if you wish to use pinned versions of direct dependencies that
        MODNet has been tested with), you can clone this git repository and make an
        editable install inside your chosen environment with `pip`:
        
        ```shell
        git clone git@github.com:ppdebreuck/modnet
        cd modnet
        conda create -n modnet python=3.9
        conda activate modnet
        pip install -r requirements.txt  # optionally use pinned requirements
        pip install -e .
        ```
        
        
        <a name="documentation"></a>
        ## Documentation
        The documentation is available at [ReadTheDocs](https://modnet.readthedocs.io).
        
        <a name="changelog"></a>
        ## Changelog
        A brief changelog can be found in the [release summaries on GitHub](https://github.com/ppdebreuck/modnet/releases).
        
        <a name="author"></a>
        ## Author
        This software was written by [Pierre-Paul De Breuck](mailto:pierre-paul.debreuck@uclouvain.be) and [Matthew Evans](https://www.github.com/ml-evs) with contributions from David Waroquiers and  Gregoire Heymans.
        For an up-to-date list, see the [Contributors on GitHub](https://github.com/ppdebreuck/modnet/graphs/contributors).
        
        <a name="License"></a>
        ## License
        
        MODNet is released under the MIT License.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
