Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning from imperfections: constructing phase diagrams from atomic
  imaging of fluctuations   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.

3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph