  Materials characterization and property measurements are a cornerstone of
material science, providing feedback from synthesis to applications.
Traditionally, a single sample is used to derive information on a single point
in composition space, and imperfections, impurities and stochastic details of
material structure are deemed irrelevant or complicating factors in analysis.
Here we demonstrate that atomic-scale studies of a single nominal composition
can provide information on a finite area of chemical space. This information
can be used to reconstruct the material properties in a finite composition and
temperature range. We develop a statistical physics-based framework that
incorporates chemical and structural data to infer effective atomic
interactions driving segregation in a La5/8Ca3/8MnO3 thin-film. A variational
autoencoder is used to determine anomalous behaviors in the composition phase
diagram. This study provides a framework for creating generative models from
diverse data and provides direct insight into the driving forces for cation
segregation in manganites.
