On-demand phase-field modeling: Three-dimensional Landau energy for HfO2 through machine learning

Abstract

The unexpected emergence of ferroelectricity in HfO2 at reduced dimensions has attracted considerable attention, as it provides a pathway toward the realization of ultrasmall ferroelectric devices. Ab initio calculations suggest that this effect arises from a unique mode coupling, in which an antipolar displacement mode stabilizes a robust polar distortion. Based on these insights, Landau-Devonshire energy models have been proposed using such lattice modes as order parameters. However, most existing models are limited to a simplified one-dimensional model because of the computational cost of ab initio calculations and the limitations of conventional Landau polynomials. Here, we constructed a three-dimensional Landau-Devonshire potential for HfO2 by employing the tetragonal, antipolar, and polar modes as coupled order parameters, based on the latest machine-learning technologies. We generated a large-scale dataset of energies over a three-dimensional structural space, with the computational cost drastically reduced through the use of machine-learning interatomic potentials, and trained a multilayer perceptron (MLP) to learn the relationship between the order parameters and the energy. The energy predicted by the MLP successfully captures the characteristic coupling behavior whereby the antipolar modes induce the polar mode. Furthermore, by extending this MLP-based Landau potential to a position-dependent functional, that is, to a phase-field modeling framework, we revealed that the polarization magnitude in thin films decreases compared with the bulk state, while the critical strain required for the onset of spontaneous polarization increases due to surface effects. This study presents a new framework for the on-demand construction of Landau energy and phase-field modeling using the latest machine-learning techniques, enabling multiscale analysis of complex ferroelectric phenomena.