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muammar committed Oct 18, 2019
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[![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)
[![GitHub release](https://img.shields.io/github/release/muammar/ml4chem.svg)](https://github.com/muammar/ml4chem/releases/latest)
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## About

This package is written in Python 3, and intends to offer modern and rich
features to perform machine learning workflows for chemical physics.
Expand All @@ -29,14 +30,14 @@ A list of features and methods are shown below.
- [Messagepack serialization](https://msgpack.org/index.html).


### Citing
## Citing

If you find this software useful, please use this DOI to cite it:

[![DOI](https://zenodo.org/badge/161847010.svg)](https://zenodo.org/badge/latestdoi/161847010)


### Documentation
## Documentation

You can read the documentation at [https://ml4chem.dev](https://ml4chem.dev)
where you can get started. It is arranged in a way that you can go through
Expand All @@ -46,12 +47,12 @@ index](https://ml4chem.dev/genindex.html) to get more information about
different classes and functions of ML4Chem.


### Dask dashboard
## Dask dashboard
![](https://raw.githubusercontent.com/muammar/ml4chem/master/docs/source/_static/dask_dashboard.png)

Note: This package is under development.

### Copyright
## 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.
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