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 0.5, 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 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. DOI:10.1016/j.cpc.2016.05.010
An amp-users mailing list has been started, for general discussions about the use and development of Amp. You can subscribe via listserv at:
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