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Machine learning-based interatomic potential development and phase transition analysis of ferroelectric hafnium dioxide

Yasantha Hetti Kankanamalage1,2,*, Yufeng Xi3, Shuai Zhang2, Sobhit Singh1,3, and Niaz Abdolrahim1,2,3

  • *Contact author: yhettika@ur.rochester.edu
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Phys. Rev. Materials 10, 014414 – Published 29 January, 2026

DOI: https://doi.org/10.1103/gkvf-t1vl

Abstract

The ferroelectric phase (Pca21, which is in orthorhombic symmetry) of hafnium dioxide (HfO2) has gained much attention due to its potential applications in nanoelectronics and advanced memory devices. However, its complex phase behavior under external stimuli, such as pressure and temperature, remains a subject of intense investigation. This study focuses on developing a machine learning-based interatomic potential (MLIP) that is trained with data from density-functional theory (DFT) calculations to simulate phase transitions and mechanical properties of HfO2. The developed MLIP predicts lattice parameters, equations of state, bulk and shear moduli, and elastic constants that closely align with DFT predictions for several phases and at various pressures. Once validated, the MLIP is used to investigate the phase transitions of ferroelectric HfO2 (Pca21) under both isobaric and constant stress conditions at elevated temperatures ranging from 200 to 2500 K. We used several complementary methods, including local symmetry identification, radial distribution function, and x-ray diffraction characterization, to identify interesting phase transitions among several competitive hafnia phases predicted from our simulations. The suggested methods uniformly reveal that under pure deviatoric condition, the system favors a transition from the orthorhombic Pca21 phase to a tetragonal (P42/nmc) phase, whereas a zero stress condition drives the system from the Pca21 phase to another orthorhombic (Pbcn) phase. These findings provide crucial insights into stress and temperature-induced phase behavior of hafnia, guiding future experimental and theoretical studies for optimizing hafnia-based ferroelectric devices.

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Phys. Rev. Materials 10, 014414– Published 29 January, 2026
  • Received 8 August 2025
  • Accepted 5 January 2026
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