To switch or not to switch - a machine learning approach for ferroelectricity
authors Neumayer, SM; Jesse, S; Velarde, G; Kholkin, AL; Kravchenko, I; Martin, LW; Balke, N; Maksymovych, P
nationality International
journal NANOSCALE ADVANCES
keywords SCANNING PROBE MICROSCOPY; PIEZORESPONSE FORCE MICROSCOPY; DOMAIN-WALLS; POLARIZATION; NANOGENERATOR; NANOSCALE; STATES; FILMS
abstract With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, etc. Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fits into a variety of machine-learning methodologies, from unsupervised classification of the origins of hysteretic response via linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis.
publisher ROYAL SOC CHEMISTRY
issn 2516-0230
year published 2020
volume 2
issue 5
beginning page 2063
ending page 2072
digital object identifier (doi) 10.1039/c9na00731h
web of science category Chemistry, Multidisciplinary; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary
subject category Chemistry; Science & Technology - Other Topics; Materials Science
unique article identifier WOS:000536705700029
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