Piezo- and pyroelectricity in Zirconia: a study with machine learned force fields
Richard Ganser, Simon Bongarz, Alexander von Mach, Luis Azevedo, Antunes, Alfred Kersch

TL;DR
This study develops a deep neural network-based interatomic force field for ZrO2, accurately simulating its piezo- and pyroelectric effects, phase transition behavior, and dielectric properties, aligning with experimental observations.
Contribution
The paper introduces a machine learning force field for ZrO2 that accurately reproduces structural, thermal, and electromechanical properties, enabling detailed molecular dynamics simulations of phase transitions and related effects.
Findings
Negative piezo- and pyroelectric coefficients at low temperatures.
Giant positive piezoelectric coefficients near phase transition.
Model explains large experimental dielectric constants.
Abstract
The discovery of very large piezo- and pyroelectric effects in ZrO2 and HfO2-based thin films opens up new opportunities to develop silicon-compatible sensor and actor devices. The effects are amplified close to the polar-orthorhombic to tetragonal phase transition temperature. Molecular dynamics is the preferred technique to simulate such effects, though its application has to solve the dilemma between sufficient accuracy and sufficient efficiency of the interatomic force field. Here we present a deep neural network-based interatomic force field of ZrO2 learned from ab initio data using a systematic learning procedure in the Deep Potential framework. The model potential is verified to predict a variety of structural and dynamic properties with an accuracy comparable to density functional theory calculations. Then the Deep Potential model is used to reproduce the different thermal…
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Taxonomy
TopicsMachine Learning in Materials Science · Ferroelectric and Negative Capacitance Devices · Semiconductor materials and devices
