Thermodynamics and dielectric response of $\text{BaTiO}_3$ by data-driven modeling

TL;DR

This paper develops a machine learning model trained on ab initio data to accurately predict the structural, energetic, and dielectric properties of BaTiO3, revealing insights into its ferroelectric transition mechanisms and phase behavior.

Contribution

The work introduces an integrated machine learning approach that models ferroelectric properties of BaTiO3 from first principles, enabling efficient predictions of phase transitions and dielectric responses.

Findings

Order-disorder transition of Ti off-centered states drives ferroelectricity.

Coupling between symmetry breaking and cell distortions leads to intermediate phases.

Model accurately reproduces dielectric response consistent with experiments.

Abstract

Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning model that describes on the same footing structural, energetic and functional properties of barium titanate ($\text{BaTiO}_3$), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. These predictions allow us to assess the microscopic mechanism of the ferroelectric transition. The presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition, even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate, partly-ordered phases. Moreover, we thoroughly probe the static and dynamical behavior of $\text{BaTiO}_3$ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Finally, we apply the polarization model to calculate dielectric response properties of the material in a fully ab initio manner, again reproducing the correct qualitative experimental behaviour.