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

TL;DR

This paper explores Bayesian active learning methods, from Gaussian Processes to hypothesis learning, to enhance automated scanning probe microscopy by integrating physics-based models and prior data for improved discovery workflows.

Contribution

It introduces a comprehensive framework for applying Bayesian active learning to SPM, combining data-driven, physics-based, and deep kernel methods for advanced experimental analysis.

Findings

Gaussian Processes enable simple data-driven modeling

Bayesian inference extends physical models for better fits

Deep kernel learning enhances complex data analysis

Abstract

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. 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.