Amp: Atomistic Machine-learning PackageΒΆ

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 0.5, released on February 24, 2017; see the Release notes page for a download link. Please see the project’s git repository for the latest development version or a place to report an issue.

You can read about Amp in the below paper; if you find Amp 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. DOI:10.1016/j.cpc.2016.05.010

Manual:

Module autodocumentation:

Indices and tables