Automated Experiments of Local Non‐Linear Behavior in Ferroelectric Materials

Abstract

An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non‐linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non‐linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip‐surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non‐linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al0.93B0.07N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al0.93B0.07N, well‐poled regions show high linear piezoelectric responses, when paired with low non‐linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non‐linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.

Explainability and human intervention in autonomous scanning probe microscopy

“…Bayesian methods 36 , 37 exemplified DKL by allowing an active learning process, 31 making decisions based on past acquired information. When implementing a workflow with DKL in an operating SPM, 20 , 23 , 31 , 38 the microscope can perform the measurement, process data, make decisions to move the probe, and initiate image scan and/or spectra measurement automatically without human intervention. This largely surpasses the speed of measurements carried out by human operators, accelerating physics discovery.…”

“…For materials synthesis, multiple approaches including pipetting robots, 1 , 2 self-driving labs, 3 , 4 , 5 and high-throughput synthesis workflows have been proposed. 6 , 7 , 8 , 9 , 10 , 11 For materials characterization, several groups have been developing automated and autonomous experiment (AE) approaches in areas including scanning transmission electron microscopy (STEM), 12 , 13 , 14 , 15 scanning probe microscopy (SPM), 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 neutron diffraction, 26 , 27 and X-ray scattering. 28 …”

“…In this process, each step of the workflow is optimized individually, whereas possible correlations between individual steps are ignored. The recent advances in approaches such as deep kernel active learning (DKL) are enabling the development of single-step, beyond-human workflows, as exemplified by the property discovery in scanning probe 20 , 23 , 31 and electron microscopy. 14 …”

A dynamic Bayesian optimized active recommender system for curiosity-driven partially “Human-in-the-loop” automated experiments

“…This dKL technique has been implemented for better exploration through active learning in experimental environments. 4,50,[61][62][63] Here, we utilized a DKL implementation from open-source AtomAI software package. 60 The overall BOARS structure remains the same, but we simply replace the standard GP with a dKL-based approach.…”

Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy

“…For example, we aim to discover which microstructural element has the best predictive capacity for the functional property encoded in polarization hysteresis loop or resonance frequency hysteresis loop such as maximal loop area, imprint bias, or more complex functionals of the loop shape. For unimodal imaging, this approach have recently been demonstrated for STEM-EELS, 4D STEM, and band excitation piezoresponse spectroscopy (BEPS) 27,32,37,38 . In these studies, we have discovered which features in image space are most predictive of the specific functionalities determined via spectral measurements, for example localization of the hysteresis loops with the maximal area at specific domain walls or emergence of low energy plasmons at the edges of 2D material flakes.…”