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Atomistic Machine-learning Package

Project description

# 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. Amp allows for the modular representation of the potential energy surface, enabling the user to specify or create descriptor and regression methods.

This project lives at: https://bitbucket.org/andrewpeterson/amp

Documentation lives at: http://amp.readthedocs.org

Users’ mailing list lives at: https://listserv.brown.edu/?A0=AMP-USERS

If you would like to compile a local version of the documentation, see the README file in the docs directory.

(This project was formerly known as “Neural”. The last stable version of Neural can be found at https://bitbucket.org/andrewpeterson/neural)

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.

Installation

You can find the installation instructions for this version of Amp in the documentation file docs/installation.rst.

Documentation

We currently host multiple versions of the documentation, which includes installation instructions, at http://amp.readthedocs.io.

You can build a local copy of the documentation for this version of Amp. You will find instructions to do this in the “Documentation” section of the file docs/develop.rst.

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