  We present a novel approach for finding and evaluating structural models of
small metallic nanoparticles. Rather than fitting a single model with many
degrees of freedom, the approach algorithmically builds libraries of
nanoparticle clusters from multiple structural motifs, and individually fits
them to experimental PDFs. Each cluster-fit is highly constrained. The
approach, called cluster-mining, returns all candidate structure models that
are consistent with the data as measured by a goodness of fit. It is highly
automated, easy to use, and yields models that are more physically realistic
and result in better agreement to the data than models based on cubic
close-packed crystallographic cores, often reported in the literature for
metallic nanoparticles.
