Using convolutional neural networks for stereological characterization
  of 3D hetero-aggregates based on synthetic STEM data
Lukas Fuchs Tom Kirstein Christoph Mahr Orkun Furat Valentin Baric Andreas Rosenauer Lutz Maedler Volker Schmidt
The structural characterization of hetero-aggregates in 3D is of great
interest, e.g., for deriving process-structure or structure-property
relationships. However, since 3D imaging techniques are often difficult to
perform as well as time and cost intensive, a characterization of
hetero-aggregates based on 2D image data is desirable, but often non-trivial.
To overcome the issues of characterizing 3D structures from 2D measurements, a
method is presented that relies on machine learning combined with methods of
spatial stochastic modeling, where the latter are utilized for the generation
of synthetic training data. This kind of training data has the advantage that
time-consuming experiments for the synthesis of differently structured
materials followed by their 3D imaging can be avoided. More precisely, a
parametric stochastic 3D model is presented, from which a wide spectrum of
virtual hetero-aggregates can be generated. Additionally, the virtual
structures are passed to a physics-based simulation tool in order to generate
virtual scanning transmission electron microscopy (STEM) images. The preset
parameters of the 3D model together with the simulated STEM images serve as a
database for the training of convolutional neural networks, which can be used
to determine the parameters of the underlying 3D model and, consequently, to
predict 3D structures of hetero-aggregates from 2D STEM images. Furthermore, an
error analysis is performed to evaluate the prediction power of the trained
neural networks with respect to structural descriptors, e.g. the
hetero-coordination number.