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        "description": "ML4Chem\n===========\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/Django.svg)](https://github.com/muammar/mkchromecast/)\n[![Build Status](https://travis-ci.com/muammar/ml4chem.svg?branch=master)](https://travis-ci.com/muammar/ml4chem)\n[![License](https://img.shields.io/badge/license-BSD-green)](https://github.com/muammar/ml4chem/blob/master/LICENSE)\n[![Downloads](https://img.shields.io/github/downloads/muammar/ml4chem/total.svg?maxAge=2592000?style=flat-square)](https://github.com/muammar/ml4chem/releases)\n[![GitHub release](https://img.shields.io/github/release/muammar/ml4chem.svg)](https://github.com/muammar/ml4chem/releases/latest)\n\n**ML4Chem** is machine learning for chemistry and Materials.\n\nThis package is written in Python 3, and intends to offer modern and rich\nfeatures to perform machine learning workflows for chemical physics.\n\nA list of features and methods are shown below.\n\n- Atom-centered Neural Networks, and Kernel Ridge Regression for the prediction\n  of total energies.\n- PyTorch backend.\n- GPU support.\n- ASE interface.\n- Completely modular. You can use any part of this package in your project.\n- Free software <3. No secrets! Pull requests and additions are more than\n  welcome!\n- Good documentation (I hope!).\n- Explicit and idiomatic: `ml4chem.get_me_a_coffee()`.\n- Distributed training in a data parallelism paradigm (mini-batches).\n- Scalability and distributed computations are powered by Dask <3.\n- Real-time tools to track status of your computations.\n- [Messagepack serialization](https://msgpack.org/index.html).\n\n\n## Citing\n\nIf you find this software useful, please use this DOI to cite it:\n\n[![DOI](https://zenodo.org/badge/161847010.svg)](https://zenodo.org/badge/latestdoi/161847010)\n\n\n## Documentation\n\nYou can read the documentation at [https://ml4chem.dev](https://ml4chem.dev)\nwhere you can get started. It is arranged in a way that you can go through\nthe theory as well as some code snippets to understand how to use this\nsoftware. Additionally, you can dive through the [module\nindex](https://ml4chem.dev/genindex.html) to get more information about\ndifferent classes and functions of ML4Chem.\n\n\n## Dask dashboard\n![](https://raw.githubusercontent.com/muammar/ml4chem/master/docs/source/_static/dask_dashboard.png)\n\nNote: This package is under development.\n\n## Copyright\nML4Chem: Machine Learning for Chemistry and Materials (ML4Chem) Copyright (c) 2019, The\nRegents of the University of California, through Lawrence Berkeley National\nLaboratory (subject to receipt of any required approvals from the U.S.\nDept. of Energy).  All rights reserved.\n\nIf you have questions about your rights to use or distribute this software,\nplease contact Berkeley Lab's Intellectual Property Office at\nIPO@lbl.gov.\n\nNOTICE.  This Software was developed under funding from the U.S. Department\nof Energy and the U.S. Government consequently retains certain rights.  As\nsuch, the U.S. Government has been granted for itself and others acting on\nits behalf a paid-up, nonexclusive, irrevocable, worldwide license in the\nSoftware to reproduce, distribute copies to the public, prepare derivative\nworks, and perform publicly and display publicly, and to permit other to do\nso.\n\n\n",
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