  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 expansion and
piezo and pyroelectric phenomena in ZrO2 with molecular dynamics calculations.
At low temperature simulating the direct effect we find negative values for the
piezo-and pyroelectric coefficients matching the ab initio calculations.
Approaching the phase transition temperature these values remain negative and
become large. Simulating the field induced effect above the phase transition
temperature we find positive, giant piezo-electric coefficients matching the
observations. The model is able to explain the large values and the sign of the
experimental observations in relation to the polar-orthorhombic to tetragonal
phase transition. The model furthermore explains the recently observed giant
dielectric constant in a similiar system.
