Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

TL;DR

This paper introduces a Bayesian optimization framework for autonomous experiments in scanning probe microscopy, significantly improving efficiency in exploring polarization dynamics in ferroelectric materials and enabling more complex spectroscopies.

Contribution

The paper develops and demonstrates a Bayesian optimization approach for autonomous SPM experiments, enhancing sampling efficiency and enabling advanced spectroscopic analysis.

Findings

Achieved ~3x increase in sampling efficiency in ferroelectric imaging.

Successfully deployed autonomous optimization on an operational SPM.

Enabled exploration of complex spectroscopies previously limited by time and stability constraints.

Abstract

Polarization dynamics in ferroelectric materials are explored via the automated experiment in Piezoresponse Force Spectroscopy. A Bayesian Optimization framework for imaging is developed and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with metrics showing gains of ~3x in the sampling efficiency. This approach opens the pathway to perform more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability, tip wear, and/or stochastic sample damage that occurs at specific, a priori unknown spatial positions. Potential improvements to the framework to enable the incorporation of more prior information and improve efficiency further are discussed.