Correlation between multi-factor phase diagrams and complex electrocaloric behaviors in PNZST antiferroelectric ceramic system

Li, Junjie; Yin, Ruowei; Li, Jianting; Su, Xiaopo; Su, Yanjing; Li, Qiao; Bai, Yang

doi:10.26599/jac.2023.9220696

Cited by 8 publications

(1 citation statement)

References 43 publications

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“…Obtaining spectra from measurements is a routine task in the natural sciences and engineering as it is one of the primary experimental methods to characterize the properties of objects [1]. Examples include the use of X-ray diffraction (XRD) for crystallography and obtaining phase information [2][3][4][5][6], X-ray absorption spectroscopy for studying physical states and local environments of atoms [7][8][9][10][11][12], polarization-electric field (P-E) hysteresis loops for identifying ferroelectricity [13,14], and dielectric temperature spectra for obtaining dielectric properties and phase transition temperatures. However, obtaining a full spectrum requires point-by-point measurements till completion, a time consuming task that may also require the use of synchrotron radiation at user facilities [15].…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics

He¹,

Wang²,

Li³

et al. 2023

Journal of Advanced Ceramics

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In material science and engineering, obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult. In this work, we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system, i.e., (Ba 1−x−y Ca x Sr y )(Ti 1−u−v−w Zr u Sn v Hf w )O 3 . The deep neural network model uses physical features as inputs and directly outputs the full spectrum, in addition to yielding the octahedral factor, Matyonov-Batsanov electronegativity, ratio of valence electron to nuclear charge, and core electron distance (Schubert) as four key descriptors. Owing to the physically meaningful features, our model exhibits better performance and generalization ability in the broader composition space of BaTiO 3 -based solid solutions. And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature. Furthermore, the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information. The prediction is also experimentally validated by typical samples of (Ba 0.85 Ca 0.15 )(Ti 0.98-x Zr x Hf 0.02 )O 3 . This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.

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