Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Ziatdinov, Maxim; Liu, Yongtao; Kelley, Kyle; Vasudevan, Rama; Kalinin, Sergei V.

doi:10.1021/acsnano.2c05303

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Journal Article · Tue Sep 06 00:00:00 EDT 2022 · ACS Nano

DOI:https://doi.org/10.1021/acsnano.2c05303· OSTI ID:1961589

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Penn State University
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States)

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

View Accepted Manuscript (DOE)

Research Organization:: Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: SC0021118

OSTI ID:: 1961589

Journal Information:: ACS Nano, Journal Name: ACS Nano Journal Issue: 9 Vol. 16; ISSN 1936-0851

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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