  We present a swift walk-through of our recent work that uses machine learning
to fit interatomic potentials based on quantum mechanical data. We describe our
Gaussian Approximation Potentials (GAP) framework, discussing a variety of
descriptors, how to train the model on total energies and derivatives and the
simultaneous use of multiple models. We also show a small example using QUIP,
the software sandbox implementation of GAP that is available for non-commercial
use.
