.. _introduction:

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Introduction
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`Amp <https://bitbucket.org/andrewpeterson/amp>`_ is an open-source package designed to easily bring machine-learning to
atomistic calculations. This allows one to predict (or really, interpolate) calculations on the potential energy surface,
by first building up a regression representation of a "train set" of atomic images. Amp calculator works by first
learning from any other calculator (usually quantum mechanical calculations) that can provide energy and forces as a
function of atomic coordinates. In theory, these predictions can take place with arbitrary accuracy approaching that of
the original calculator.

Amp is designed to integrate closely with the `Atomic Simulation Environment <https://wiki.fysik.dtu.dk/ase/>`_ (ASE).
As such, the interface is in pure python, although several compute-heavy parts of the underlying codes also have fortran
versions to accelerate the calculations. The close integration with ASE means that any calculator that works with ASE
- including EMT, GPAW, DACAPO, VASP, NWChem, and Gaussian - can easily be used as the parent method.