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A continuing challenge in atomic resolution microscopy is to identifysignificant structural motifs and their assembly rules in synthesized materialswith limited observations. Here we propose and validate a simple and effectivehybrid generative model capable of predicting unseen domain boundaries in apotassium sodium niobate thin film from only a small number of observations,without expensive first-principles calculation. Our results demonstrate thatcomplicated domain boundary structures can arise from simple interpretablelocal rules, played out probabilistically. We also found new significanttileable boundary motifs and evidence that our system creates domain boundarieswith the highest entropy. More broadly, our work shows that simple yetinterpretable machine learning models can help us describe and understand thenature and origin of disorder in complex materials.

A multiscale generative model to understand disorder in domain boundaries