Classifying Surface Probe Images in Strongly Correlated Electronic Systems Via Machine Learning

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,[1, 2] 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. By adding machine learning algorithms to our previously developed cluster techniques, we expect the complementary nature of the two techniques to further facilitate our understanding of correlated materials. [1] B. Phillabaum et al., Nat. Commun. 3, 915 (2012); [2] S. Liu et al., Phys. Rev. Lett. 116, 036401 (2016).