Metadata-Version: 2.1
Name: ml4chem
Version: 0.0.8
Summary: Machine learning for chemistry and materials.
Home-page: https://github.com/muammar/ml4chem
Author: Muammar El Khatib
Author-email: muammarelkhatib@gmail.com
License: UNKNOWN
Description: ![alt text](https://raw.githubusercontent.com/muammar/ml4chem/master/docs/source/_static/ml4chem.png "Logo")
        
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        ## About
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/Django.svg)](https://github.com/muammar/mkchromecast/)
        [![Build Status](https://travis-ci.com/muammar/ml4chem.svg?branch=master)](https://travis-ci.com/muammar/ml4chem)
        [![License](https://img.shields.io/badge/license-BSD-green)](https://github.com/muammar/ml4chem/blob/master/LICENSE)
        [![Downloads](https://img.shields.io/github/downloads/muammar/ml4chem/total.svg?maxAge=2592000?style=flat-square)](https://github.com/muammar/ml4chem/releases)
        ![PyPI - Downloads](https://img.shields.io/pypi/dm/ml4chem)
        [![GitHub release](https://img.shields.io/github/release/muammar/ml4chem.svg)](https://github.com/muammar/ml4chem/releases/latest)
        [![Documentation Status](https://readthedocs.org/projects/ml4chem/badge/?version=latest)](https://ml4chem.readthedocs.io/en/latest/?badge=latest)
        [![Slack channel](https://img.shields.io/badge/slack-ml4chem-yellow.svg?logo=slack)](https://ml4chem.slack.com/)
        
        
        
        ML4Chem is a package to deploy machine learning for chemistry and materials
        science. It is written in Python 3, and intends to offer modern and rich
        features to perform machine learning (ML) workflows for chemical physics.
        
        A list of features and ML algorithms are shown below.
        
        - PyTorch backend.
        - Completely modular. You can use any part of this package in your project.
        - Free software <3. No secrets! Pull requests and additions are more than
          welcome!
        - Documentation (work in progress).
        - Explicit and idiomatic: `ml4chem.get_me_a_coffee()`.
        - Distributed training in a data parallel paradigm aka mini-batches.
        - Scalability and distributed computations are powered by Dask.
        - Real-time tools to track status of your computations.
        - [Messagepack serialization](https://msgpack.org/index.html).
        
        
        ## Citing
        
        If you find this software useful, please use this DOI to cite it:
        
        [DOI: 10.5281/zenodo.3471761](https://doi.org/10.5281/zenodo.3471761)
        
        
        ## Documentation
        
        To get started,  read the documentation at
        [https://ml4chem.dev](https://ml4chem.dev). It is arranged in a way that you
        can go through the theory as well as some code snippets to understand how to
        use this software. Additionally, you can dive through the [module
        index](https://ml4chem.dev/genindex.html) to get more information about
        different classes and functions of ML4Chem.
        
        
        ## Visualizations
        ![](https://raw.githubusercontent.com/muammar/ml4chem/master/docs/source/_static/dask_dashboard.png)
        
        
        Note: This package is under development.
        
        ## Copyright
        ML4Chem: Machine Learning for Chemistry and Materials (ML4Chem) Copyright (c) 2019, The
        Regents of the University of California, through Lawrence Berkeley National
        Laboratory (subject to receipt of any required approvals from the U.S.
        Dept. of Energy).  All rights reserved.
        
        If you have questions about your rights to use or distribute this software,
        please contact Berkeley Lab's Intellectual Property Office at
        IPO@lbl.gov.
        
        NOTICE.  This Software was developed under funding from the U.S. Department
        of Energy and the U.S. Government consequently retains certain rights.  As
        such, the U.S. Government has been granted for itself and others acting on
        its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
        Software to reproduce, distribute copies to the public, prepare derivative
        works, and perform publicly and display publicly, and to permit other to do
        so.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
