Machine Learning in Scanning probe microscopy: accelerating imaging, enhancing resolution and Bayesian methodologies for theory-experiment matching*

Scanning probe microscopy (SPM) [1], including a variety of modes in atomic force microscopy and scanning tunneling microscopy, have yielded tremendous insights into the functioning of materials at the atomic and nanoscale, and remain a major tool of nanoscience in fields as diverse as condensed matter physics, catalysis and surface chemistry, and molecular biology. Despite the proliferation of SPMs, most are still wedded to traditional paradigms of limited data acquisition, analysis by hand-crafted simple models, and a decided lack of uncertainty quantification.

In this talk, I will discuss how SPM can be greatly enhanced via careful and tailored use of machine/statistical learning methodologies in every aspect, from data acquisition to real-time analytics to model comparison and selection. I will show that Gaussian process regression and active learning schemes can enable increases in sampling efficiency in large dimensional spaces, enabling new experiments that were previously unfeasible. Moreover, complete information acquisition in conjunction with statistical learning methodologies can enable new SPM techniques with enhanced spectral and spatial resolution that are orders of magnitude faster than existing state of the art techniques [2]. Finally, the use of Bayesian methodologies for model fitting and model selection can enable a rigorous uncertainty-quantified answer regarding the appropriate cantilever dynamics model to employ in analyzing dynamic atomic force microscopy data. These methods that are developed point towards an autonomous future of ‘self-exploring’ SPM systems for physics knowledge generation.

References
[1] “Scanning Probe Microscopy, from Sublime to Ubiquitous,” https://journals.aps.org/prl/scanning-probe-microscopy(2016).
[2] Somnath et al., Nat Commun. 9, 513 (2018)