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KFM: Fachverband Kristalline Festkörper und deren Mikrostruktur

KFM 23: Focus Session: Fermi level engineering of functional ceramics

KFM 23.3: Talk

Thursday, March 21, 2024, 10:20–10:40, E 124

Machine Learning Potential for BaTiO₃ — •Amit Sehrawat, Jochen Rohrer, and Karsten Albe — Materials Modelling Division, Otto-Berndt-Str. 3 64206 Darmstadt

Barium titanate (BaTiO₃, BTO), a well-known perovskite oxide, undergoes intricate ferroelectric phase transitions. These transitions are characterized by a shift from a paraelectric cubic phase at high temperatures to a sequence of low-temperature phases (Cubic -> Tetragonal -> Orthorhombic -> Rhombohedral), predominantly driven by antiferrodistortive modes. Although ab-initio molecular dynamics is used to explore the finite-temperature properties, the high computational cost and scaling to only a few hundred atoms restricts the study for a longer time and length scale. Conversely, classical molecular dynamics simulations, though efficient in understanding atomic-scale dynamics, often lack accuracy compared to first-principles-based methods. To overcome these limitations, we develop a machine learning interatomic potential (MLIP) for BaTiO₃, based on Atomic Cluster Expansion (ACE) formalism using data from density functional theory (DFT) calculations. The ML potential achieves DFT-level accuracy while facilitating simulations over significantly longer time and length scales. Using trained potential, our research investigates the temperature-driven cubic-to-Rhombohedral phase transition in BTO and examines the influence of pressure on the transition temperature.

Keywords: Machine Learning Potentials; BaTiO3; Phase Transition