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O: Fachverband Oberflächenphysik

O 43: Frontiers of Electronic Structure Theory: Focus on Artificial Intelligence Applied to Real Materials 1

O 43.4: Vortrag

Mittwoch, 7. September 2022, 11:15–11:30, S054

Structural phases and thermodynamics of BaTiO₃ from an integrated machine learning model — •Lorenzo Gigli¹, Max Veit¹, Michele Kotiuga², Giovanni Pizzi², Nicola Marzari², and Michele Ceriotti¹ — ¹Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland — ²Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

Modeling the ferroelectric transition of any given material requires three key ingredients: (1) a model of the potential energy surface, that describes the energetic response to a structural distortion; (2) the free energy surface sampled at the relevant, finite-temperature conditions; and; (3) the polarization of individual configurations that determines the observed polarization and the phase transitions. To this aim, we make use of an integrated machine-learning framework, based on a combination of an interatomic potential and a microscopic polarization model, which we use to run Molecular Dynamics simulations of ferroelectrics with the same accuracy of the underlying DFT method, on time and length scales that are not accessible to direct ab-initio modeling. This allows us to uncover the microscopic nature of the ferroelectric transition in barium titanate (BaTiO₃) and to identify the presence of an order-disorder transition as the main driver of ferroelectricity. The framework also allows us to reconstruct the temperature-dependent BaTiO₃ phase diagram, with first-of-its-kind accuracy.