  Scanning probe experiments such as scanning tunneling microscopy (STM) and
atomic force microscopy (AFM) on strongly correlated electronic systems often
reveal complex pattern formation on multiple length scales. By studying the
universal scaling in these images, we have shown in several distinct correlated
electronic systems that the pattern formation is driven by proximity to a
disorder-driven critical point, revealing a unification of the pattern
formation in these materials. As an alternative approach to this image
classification problem of novel materials, here we report the first
investigation of the machine learning method to determine which underlying
physical model is driving pattern formation in a system. Using a neural network
architecture, we are able to achieve 97% accuracy on classifying configuration
images from three models with Ising symmetry. This investigation also
demonstrates that machine learning can capture the implicit universal behavior
of a physical system. This broadens our understanding of what machine learning
can do, and we expect more synergy between machine learning and condensed
matter physics in the future.
