Pair-Variational Autoencoders (PairVAE) for Linking and
  Cross-Reconstruction of Characterization Data from Complementary Structural
  Characterization Techniques
Shizhao Lu Arthi Jayaraman
In material research, structural characterization often requires multiple
complementary techniques to obtain a holistic morphological view of the
synthesized material. Depending on the availability of and accessibility of the
different characterization techniques (e.g., scattering, microscopy,
spectroscopy), each research facility or academic research lab may have access
to high-throughput capability in one technique but face limitations (sample
preparation, resolution, access time) with other techniques(s). Furthermore,
one type of structural characterization data may be easier to interpret than
another (e.g., microscopy images are easier to interpret than small angle
scattering profiles). Thus, it is useful to have machine learning models that
can be trained on paired structural characterization data from multiple
techniques so that the model can generate one set of characterization data from
the other. In this paper we demonstrate one such machine learning workflow,
PairVAE, that works with data from Small Angle X-Ray Scattering (SAXS) that
presents information about bulk morphology and images from Scanning Electron
Microscopy (SEM) that presents two-dimensional local structural information of
the sample. Using paired SAXS and SEM data of novel block copolymer assembled
morphologies [open access data from Doerk G.S., et al. Science Advances. 2023
Jan 13;9(2): eadd3687], we train our PairVAE. After successful training, we
demonstrate that the PairVAE can generate SEM images of the block copolymer
morphology when it takes as input that sample's corresponding SAXS 2D pattern,
and vice versa. This method can be extended to other soft materials
morphologies as well and serves as a valuable tool for easy interpretation of
2D SAXS patterns as well as creating a database for other downstream
calculations of structure-property relationships.