  Determination of crystal structures of nanocrystalline or amorphous compounds
is a great challenge in solid states chemistry and physics. Pair distribution
function (PDF) analysis of X-Ray or neutron total scattering data has proven to
be a key element in tackling this challenge. However, in most cases a reliable
structural motif is needed as starting configuration for structure refinements.
Here, we present an algorithm that is able to determine the crystal structure
of an unknown compound by means of an on-the-fly trained machine learning model
that combines density functional theory (DFT) calculations with comparison of
calculated and measured PDFs for global optimization in an artificial
landscape. Due to the nature of this landscape, even metastable configurations
can be determined.
