Revealing Patterns in Scanning Probe Microscopy Data via Machine Learning Techniques

Machine Learning (ML) techniques have become prevalent in many diverse fields of research, with the goal of helping extract information from large, complex datasets. Its penetration into condensed matter physics is still however relatively shallow, even for application to results from techniques such as scanning tunneling microscopy (STM), where the image-based nature of the data would naturally seem to lend itself to now standard ML investigations. Here we present results of ML techniques applied to both topographic and spectroscopic STM data, demonstrating the power of these techniques to reveal previously hidden connections between the two and hence help improve our understanding of the relationship between structure and electronic properties at the atomic scale.