  We have applied recent machine learning advances, deep convolutional neural
network, to three-dimensional (voxels) soft matter data, generated by Molecular
Dynamics computer simulation. We have focused on the structural and phase
properties of a coarse-grained model of hydrated ionic surfactants. We have
trained a classifier able to automatically detect the water quantity absorbed
in the system, therefore associating to each hydration level the corresponding
most representative nano-structure. Based on the notion of transfer learning,
we have next applied the same network to the related polymeric ionomer Nafion,
and have extracted a measure of the similarity of these configurations with
those above. We demonstrate that on this basis it is possible to express the
static structure factor of the polymer at fixed hydration level as a
superposition of those of the surfactants at multiple water contents. We
suggest that such a procedure can provide a useful, agnostic, data-driven,
precise description of the multi-scale structure of disordered materials,
without resorting to any a-priori model picture.
