  The structural solution problem can be a daunting and time consuming task.
Especially in the presence of impurity phases, current methods such as indexing
become more unstable. In this work, we apply the novel approach of
semi-supervised learning towards the problem of identifying the Bravais lattice
and the space group of inorganic crystals. Our semi-supervised generative deep
learning model can train on both labeled data -- diffraction patterns with the
associated crystal structure -- and unlabeled data, diffraction patterns that
lack this information. This approach allows our models to take advantage of the
troves of unlabeled data that current supervised learning approaches cannot,
which should result in models that can more accurately generalize to real data.
In this work, we classify powder diffraction patterns into all 14 Bravais
lattices and 144 space groups (we limit the number due to sparse coverage in
crystal structure databases), which covers more crystal classes than other
studies. Our models also drastically outperform current deep learning
approaches for both space group and Bravais Lattice classification using less
training data.
