.. Amp documentation master file, created by
   sphinx-quickstart on Thu Jul 30 17:27:50 2015.
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Amp: Atomistic Machine-learning Package
=======================================

Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.
This project is being developed at Brown University in the School of Engineering, primarily by **Andrew Peterson** and **Alireza Khorshidi**, and is released under the GNU General Public License.

The latest stable release of Amp is version 0.6, released on July 31, 2017; see the :ref:`ReleaseNotes` page for a download link.
Please see the project's `git repository <https://bitbucket.org/andrewpeterson/amp>`_ for the latest development version or a place to report an issue.

You can read about Amp in the below paper; if you find this project useful, we would appreciate if you cite this work:

    Khorshidi & Peterson, "Amp: A modular approach to machine learning in atomistic simulations", *Computer Physics Communications* 207:310-324, 2016. |amp_paper|


.. |amp_paper| raw:: html

   <a href="http://dx.doi.org/10.1016/j.cpc.2016.05.010" target="_blank">DOI:10.1016/j.cpc.2016.05.010</a>

**News**:

An amp-users mailing list has been started, for general discussions about the use and development of Amp. You can subscribe via listserv at:

https://listserv.brown.edu/?A0=AMP-USERS

**Manual**:

.. toctree::
   :maxdepth: 1

   introduction.rst
   installation.rst
   useamp.rst
   community.rst
   theory.rst
   credits.rst
   releasenotes.rst
   examplescripts.rst
   analysis.rst
   building.rst
   moredescriptor.rst
   moremodel.rst
   gaussian.rst
   tensorflow.rst
   databases.rst
   develop.rst

**Module autodocumentation**:

.. toctree::
   :maxdepth: 1

   modules/main.rst
   modules/descriptor.rst
   modules/model.rst
   modules/regression.rst
   modules/utilities.rst
   modules/analysis.rst


**Indices and tables**

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`