2018/02/24 by L. Burzawa, S. Liu, Shuo Liu +1
Materials Science · Physics and Astronomy · #Artificial neural network #Atomic force microscopy #Deep learning #Electronic and Structural Properties of Oxides #Ising model #Machine Learning in Materials Science #Pattern recognition (psychology) #Quantum many-body systems #Scaling #Scanning tunneling microscope #Surface (topology) #cond-mat.dis-nn #cond-mat.stat-mech #cond-mat.str-el
paper · pdf · doi:10.1103/physrevmaterials.3.033805
published as Phys. Rev. Materials 3, 033805 (2019) · 5 pages, 4 figures
arxiv created 2018/02/24 · openalex created_date 2018/03/06 · openalex publication_date 2019/03/29 · arxiv updated 2019/04/03 · openalex updated_date 2026/08/06
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 an 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.