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With the advent of increasingly elaborate experimental techniques in physics,chemistry and materials sciences, measured data are becoming bigger and morecomplex. The observables are typically a function of several stimuli resultingin multidimensional data sets spanning a range of experimental parameters. Asan example, a common approach to study ferroelectric switching is to observeeffects of applied electric field, but switching can also be enacted bypressure and is influenced by strain fields, material composition, temperature,time, etc. Moreover, the parameters are usually interdependent, so that theirdecoupling toward univariate measurements or analysis may not bestraightforward. On the other hand, both explicit and hidden parameters providean opportunity to gain deeper insight into the measured properties, providedthere exists a well-defined path to capture and analyze such data. Here, weintroduce a new, two-dimensional approach to represent hysteretic response of amaterial system to applied electric field. Utilizing ferroelectric polarizationas a model hysteretic property, we demonstrate how explicit consideration ofelectromechanical response to two rather than one control voltages enablessignificantly more transparent and robust interpretation of observedhysteresis, such as differentiating between charge trapping andferroelectricity. Furthermore, we demonstrate how the new data representationreadily fits into a variety of machinelearning methodologies, from unsupervisedclassification of the origins of hysteretic response via linear clusteringalgorithms to neural-network-based inference of the sample temperature based onthe specific morphology of hysteresis.

To switch or not to switch -- a machine learning approach for ferroelectricity