  Machine learning models based on convolutional neural networks have been used
for predicting space groups of crystal structures from their atomic pair
distribution function (PDF). However, the PDFs used to train the model are
calculated using a fixed set of parameters that reflect specific experimental
conditions, and the accuracy of the model when given PDFs generated with
different choices of these parameters is unknown. In this paper, we report that
the results of the top-1 accuracy and top-6 accuracy are robust when applied to
PDFs of different choices of experimental parameters $r_\text{max}$,
$Q_\text{max}$, $Q_\text{damp}$ and atomic displacement parameters.
