Metadata-Version: 2.1
Name: modnet
Version: 0.1.11.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)
        
        ## Table of contents
        - [Introduction](#introduction)
        - [How to install](#install)
        - [Usage](#usage)
        - [Pretrained models](#pretrained)
        - [Stored MODData](#stored-moddata)
        - [Documentation](#documentation)
        - [Getting started](#getting-started)
          - [MODData](#moddata)
          - [MODNetModel](#modnetmodel)
        - [Author](#author)
        - [License](#license)
        
        
        
        
        <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**.
        
        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:
        
        - _Machine learning materials properties for small datasets_, De Breuck *et al.* (2020), [arXiv:2004.14766](https://arxiv.org/abs/2004.14766).
        
        - _Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet_, De Breuck *et al.* (2021), [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
        
        MODNet can be installed via 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
        The first versions of this software were written by [Pierre-Paul De Breuck](mailto:pierre-paul.debreuck@uclouvain.be), with contributions from Matthew Evans (v0.1.7+).
        
        <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
