Metadata-Version: 2.1
Name: modnet
Version: 0.1.12.dev0
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/workflow/status/ppdebreuck/modnet/Run%20tests?logo=github)](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: [ml-evs/modnet-matbench](https://github.com/ml-evs/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:
        
        ```shell
        conda create -n modnet python=3.8
        ```
        
        activate the environment:
        
        ```shell
        conda activate modnet
        ```
        
        Then, install pymatgen v2020.8.13 with conda, which will bundle several pre-built dependencies (e.g., numpy, scipy):
        
        ```shell
        conda install -c conda-forge pymatgen=2020.8.13
        ```
        
        Finally, install MODNet from PyPI with pip:
        
        ```bash
        pip install modnet
        ```
        
        <a name="documentation"></a>
        ## Documentation
        The documentation is available at [ReadTheDocs](https://modnet.readthedocs.io).
        
        <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: 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
