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|Title:||To switch or not to switch - a machine learning approach for ferroelectricity|
|Authors:||Neumayer, S. M.|
Kholkin, A. L.
Martin, L. W.
|Publisher:||Royal Society of Chemistry|
|Citation:||To switch or not to switch - a machine learning approach for ferroelectricity / S. M. Neumayer, S. Jesse, G. Velarde, A. L. Kholkin, et al. . — DOI 10.1039/c9na00731h // Nanoscale Advances. — 2020. — Vol. 5. — Iss. 2. — P. 2063-2072.|
|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 responsevialinear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis. © The Royal Society of Chemistry 2020.|
MACHINE LEARNING APPROACHES
|metadata.dc.description.sponsorship:||National Science Foundation, NSF: DMR-1708615|
Basic Energy Sciences, BES
Army Research Office, ARO: W911NF-14-1-0104
Experiments on PZT, data analysis and manuscript preparation were supported by the Division of Materials Science and Engineering, Basic Energy Sciences, US Department of Energy (S. N., N. B., P. M.). Experiments were conducted at and supported by (S. J.) the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. S. M. N. would like to thank Katia Gallo and Mohammad Amin Baghban for providing the lithium niobate sample. G. V. acknowledges support from the National Science Foundation under grant DMR-1708615. L. W. M. acknowledges support from the Army Research Office under grant W911NF-14-1-0104. Part of this work was developed within the scope of the project CICECO-Aveiro Institute of Materials, refs. UIDB/50011/2020 & UIDP/ 50011/2020, nanced by national funds through the FCT/MEC.
|Appears in Collections:||Научные публикации, проиндексированные в SCOPUS и WoS CC|
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