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Amp: The Atomistic Machine-learning Package v0.5

Khorshidi, Alireza; Ulissi, Zachary; El Khatib, Muammar; Peterson*, Andrew

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Khorshidi, Alireza</dc:creator>
  <dc:creator>Ulissi, Zachary</dc:creator>
  <dc:creator>El Khatib, Muammar</dc:creator>
  <dc:creator>Peterson*, Andrew</dc:creator>
  <dc:description>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, allowing the user to specify or create descriptor and regression methods.

This project lives at:

Documentation lives at:

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

*Corresponding author:</dc:description>
  <dc:title>Amp: The Atomistic Machine-learning Package v0.5</dc:title>
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