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.

Please see the project’s git repository for the latest version, news, 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:

• Introduction
• Theory
• Credits
• Release notes / Revision history
• Installation
• Using Amp
• Example scripts
• Analysis
• Neural network model
• Building modules
• More on descriptors
• More on models
• TensorFlow
• Fingerprint databases
• Development

Module autodocumentation:

• Main
• Descriptor
• Model
• Regression
• Utilities
• Analysis

Indices and tables

• Index
• Module Index
• Search Page