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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Machine learning-driven exploration of temperature and pressure dependent phase transitions in ferroelectric Hafnium dioxide

Author(s)

Yasantha Migara Hetti Kankanamalage, Yufeng Xi, Shuai Zhang, Sobhit Singh, Niaz Abdolrahim

On-Site Speaker (Planned)

Yasantha Migara Hetti Kankanamalage

Abstract Scope

We developed a machine learning-based interatomic potential (MLIP) for Hafnium dioxide (HfO₂), trained on density functional theory (DFT) datasets and validated through benchmarks including lattice parameters, equations of state, and elastic constants. Molecular dynamics simulations using the validated MLIP explored the stability and behavior of the ferroelectric Pca2₁ phase at elevated temperatures and varying pressure conditions. To correctly classify newly formed phases during the temperature ramping simulations, additional machine learning models analyzing local symmetry, radial distribution functions, and simulated X-ray diffraction patterns were implemented. Our results highlight distinct energy pathways dependent on pressure conditions: under an isobaric ensemble with purely deviatoric stress, the ferroelectric Pca2₁ phase transitions preferentially to the tetragonal P4₂/nmc phase, whereas under a constant-stress ensemble with zero stress conditions, the Pbcn phase becomes energetically favorable. These findings enhance our understanding of pressure-dependent phase stabilization mechanisms in the ferroelectric phase of HfO₂, crucial for electronics and memory applications.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation, Computational Materials Science & Engineering

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