Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Liu, Yongtao; Kelley, Kyle P.; Vasudevan, Rama K.; Funakubo, Hiroshi; Ziatdinov, Maxim A.; Kalinin, Sergei V.

doi:10.1038/s42256-022-00460-0

Title: Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Journal Article · Mon Apr 04 00:00:00 EDT 2022 · Nature Machine Intelligence

DOI:https://doi.org/10.1038/s42256-022-00460-0· OSTI ID:1864449

Liu, Yongtao ^[1]; Kelley, Kyle P. ^[1]; Vasudevan, Rama K. ^[1]; Funakubo, Hiroshi ^[2];

^[1];

^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Tokyo Institute of Technology (Japan)

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. The proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC05-00OR22725; SC0021118

OSTI ID:: 1864449

Alternate ID(s):: OSTI ID: 1961609; OSTI ID: 1961617

Journal Information:: Nature Machine Intelligence, Vol. 4, Issue 4; ISSN 2522-5839

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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