Deep learning of accurate force field of ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>HfO</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:math>
Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Abstract
The discovery of ferroelectricity in ${\mathrm{HfO}}_{2}$-based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density nonvolatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at finite temperatures. Molecular dynamics is an ideal technique for investigating dynamical processes on large length and time scales, though its applications to new materials are often hindered by the limited availability and accuracy of classical force fields. Here we present a deep neural network--based interatomic force field of ${\mathrm{HfO}}_{2}$ learned from ab initio data using a concurrent learning procedure. The model potential is able to predict structural properties such as elastic constants, equation of states, phonon dispersion relationships, and phase transition barriers of various hafnia polymorphs with accuracy comparable with density functional theory calculations. The validity of this model potential is further confirmed by the reproduction of experimental sequences of temperature-driven ferroelectric-paraelectric phase transitions of ${\mathrm{HfO}}_{2}$ with isobaric-isothermal ensemble molecular dynamics simulations. We suggest a general approach to extend the model potential of ${\mathrm{HfO}}_{2}$ to related material systems including dopants and defects.