Electric Field-Induced Phase Transitions and Hysteresis in Ferroelectric HfO2 Captured with Machine Learning Potential

TL;DR

This paper introduces a machine learning potential for simulating electric field effects in ferroelectric HfO2, enabling large-scale, accurate atomic simulations of phase transitions and hysteresis crucial for device applications.

Contribution

The authors develop a novel machine learning potential coupled with an in-situ Born effective charge model for HfO2, allowing efficient and accurate simulation of electric-field-induced phenomena.

Findings

Reproduces hysteresis loops consistent with AIMD

Captures field-induced phase transitions and polarization switching

Reveals electric-field-induced polarization activation in monoclinic phase

Abstract

Electric field-induced studies, including phase transition and polarization hysteresis, for ferroelectric HfO2 at the atomic scale are critical since they can largely affect its application in ferroelectric and dielectric devices. However, conventional first-principles approaches are computationally limited in capturing large-scale atomic dynamics under realistic field conditions. Here, to enable electric-field-driven molecular dynamics simulations, we develop a machine learning potential (MLP) tailored for HfO2, coupled with an in-situ Born effective charge (BEC) model. This framework enables us to capture key phenomena, including field-induced phase transitions, polarization switching, and strain-dependent dielectric responses, with high fidelity and computational efficiency. Notably, we reproduce hysteresis loops and phase transition barriers consistent with AIMD results and reveal possible electric-field-induced polarization activation in the monoclinic phase. Our approach offers a scalable and transferable tool for atomistic exploration of functional oxides and paves the way for data-driven design of ferroelectric devices.