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Machine Learning Based Distinguishing between Ferroelectric and Non‐Ferroelectric Polarization–Electric Field Hysteresis Loops

Machine Learning Based Distinguishing between Ferroelectric and Non‐Ferroelectric... The polarization–electric field (P–E) hysteresis loop is one of the most important criteria for identifying ferroelectricity. However, a P–E loop with apparent hysteresis window can be generated from non‐ferroelectric sources such as leakage current. So far distinguishing between ferroelectric and non‐ferroelectric loops is still performed in a manual way, which can be error prone and time consuming, particularly when the loops are not easily distinguishable and the number of loops to be identified is large. Here, two machine learning (ML) approaches are developed, one using the polarization values along the P–E loops as the input dataset (termed as “value‐based” approach) and the other using the loop images as the input dataset (termed as “image‐based” approach), to identify the P–E loops as ferroelectric or non‐ferroelectric. The value‐ and image‐based ML approaches achieve identification accuracies as high as 93.08% and 87.42%, respectively. In addition, it is tested that both approaches complete an identification of about 160 loops in very short time (≈1.0 s). The high accuracy and efficiency therefore demonstrate that the ML approaches significantly outperform the manual way for distinguishing ferroelectric from non‐ferroelectric P–E loops, which may greatly facilitate the research on ferroelectrics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advanced Theory and Simulations Wiley

Machine Learning Based Distinguishing between Ferroelectric and Non‐Ferroelectric Polarization–Electric Field Hysteresis Loops

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Publisher
Wiley
Copyright
© 2020 Wiley‐VCH GmbH
eISSN
2513-0390
DOI
10.1002/adts.202000106
Publisher site
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Abstract

The polarization–electric field (P–E) hysteresis loop is one of the most important criteria for identifying ferroelectricity. However, a P–E loop with apparent hysteresis window can be generated from non‐ferroelectric sources such as leakage current. So far distinguishing between ferroelectric and non‐ferroelectric loops is still performed in a manual way, which can be error prone and time consuming, particularly when the loops are not easily distinguishable and the number of loops to be identified is large. Here, two machine learning (ML) approaches are developed, one using the polarization values along the P–E loops as the input dataset (termed as “value‐based” approach) and the other using the loop images as the input dataset (termed as “image‐based” approach), to identify the P–E loops as ferroelectric or non‐ferroelectric. The value‐ and image‐based ML approaches achieve identification accuracies as high as 93.08% and 87.42%, respectively. In addition, it is tested that both approaches complete an identification of about 160 loops in very short time (≈1.0 s). The high accuracy and efficiency therefore demonstrate that the ML approaches significantly outperform the manual way for distinguishing ferroelectric from non‐ferroelectric P–E loops, which may greatly facilitate the research on ferroelectrics.

Journal

Advanced Theory and Simulations – Wiley

Published: Sep 1, 2020

Keywords: ; ; ; ;

References