Regensburg 2022 – scientific programme

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O: Fachverband Oberflächenphysik

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

O 43.5: Talk

Wednesday, September 7, 2022, 11:30–11:45, S054

Dielectric properties of BaTiO₃ from an integrated machine-learning model — •Max Veit¹, Lorenzo Gigli¹, Michele Kotiuga², Giovanni Pizzi², Nicola Marzari², and Michele Ceirotti¹ — ¹Laboratory for Computational Science and Modeling (COSMO), Ecole Polytechnique Fédérale de Lausanne, Lausanne, CH — ²Laboratory for Theory and Simulation of Materials (THEOS), Ecole Polytechnique Fédérale de Lausanne, Lausanne, CH

Modeling the finite-temperature behavior of ferroelectric materials from first principles has always been challenging due to the large supercells and long simulation times required for adequate sampling. Here we demonstrate the use of an integrated machine learning (ML) model of the potential energy and polarization surfaces of barium titanate (BaTiO₃) to overcome these difficulties and run long MD simulations with DFT accuracy. We use these simulations to compute the frequency-dependent dielectric response function, finding a spectrum qualitatively similar that obtained with previous effective-Hamiltonian simulations as well as to experimentally measured profiles, with some remaining discrepancies that we trace back to the underlying DFT model. Finally, we discuss possible extensions of the model to explicitly include long-range interactions, previously included only in an implicit, emergent manner. We expect this integrated, generally applicable modeling technique to become a valuable tool for elucidating the ferroelectric behavior of a wide variety of materials.