  We present a method for predicting the space group of a structure given a
calculated or measured atomic pair distribution function (PDF) from that
structure. The method utilizes machine learning models trained on more than
100,000 PDFs calculated from structures in the 45 most heavily represented
space groups. In particular, we present a convolutional neural network (CNN)
model which yields a promising result that it correctly identifies the space
group among the top-6 estimates 91.9~\% of the time. The CNN model also
successfully identifies space groups on 12 out of 15 experimental PDFs. We
discuss interesting aspects of the failed estimates, which indicate that the
CNN is failing in similar ways as conventional indexing algorithms applied to
conventional powder diffraction data. This preliminary success of the CNN model
shows the possibility of model-independent assessment of PDF data on a wide
class of materials.
