  A continuing challenge in atomic resolution microscopy is to identify
significant structural motifs and their assembly rules in synthesized materials
with limited observations. Here we propose and validate a simple and effective
hybrid generative model capable of predicting unseen domain boundaries in a
potassium sodium niobate thin film from only a small number of observations,
without expensive first-principles calculation. Our results demonstrate that
complicated domain boundary structures can arise from simple interpretable
local rules, played out probabilistically. We also found new significant
tileable boundary motifs and evidence that our system creates domain boundaries
with the highest entropy. More broadly, our work shows that simple yet
interpretable machine learning models can help us describe and understand the
nature and origin of disorder in complex materials.
