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Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling

Lorenzo Gigli1*, Max Veit1*, Michele Kotiuga2*, Giovanni Pizzi2*, Nicola Marzari2*, Michele Ceriotti1*

1 Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

2 Theory 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

* Corresponding authors emails: lorenzo.gigli@epfl.ch, max.veit@epfl.ch, michele.kotiuga@epfl.ch, giovanni.pizzi@epfl.ch, nicola.marzari@epfl.ch, michele.ceriotti@epfl.ch
DOI10.24435/materialscloud:9g-k6 [version v1]

Publication date: Jun 29, 2022

How to cite this record

Lorenzo Gigli, Max Veit, Michele Kotiuga, Giovanni Pizzi, Nicola Marzari, Michele Ceriotti, Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling, Materials Cloud Archive 2022.88 (2022), doi: 10.24435/materialscloud:9g-k6.

Description

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 (ML) model that describes on the same footing structural, energetic and functional properties of barium titanate (BaTiO₃), 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. The ML model allows us to thoroughly probe the static and dynamical behavior of BaTiO₃ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Furthermore, we apply the polarization model to calculate dielectric response properties of the material in a fully ab-initio manner. This archive provides all the relevant data and input files that were used to fit the ML interatomic potential and the polarization model used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction and the validation of the ML model. Furthermore, it provides input files and first few snapshots of all the molecular dynamics trajectories needed to investigate the thermodynamics and dielectric properties of BaTiO₃.

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Files

File name Size Description
BaTiO3_MaterialsCloud.zip
MD5md5:a09c6d31211e553829fdc75cfc2ecf41
676.3 MiB Contains directories with a brief README and the data to reproduce the figures in the main text and the Supplemental Material
DFT_trainingset_validation-002.aiida
MD5md5:d3052b168879ed1bf67e5957ca0e24e1
Open this AiiDA archive on renkulab.io (https://renkulab.io/)
2.3 GiB AiiDa archive (AiiDA version 1.6.3) of all relevant DFT calculations

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

Keywords

EPFL SNSF MARVEL Swissuniversities Samsung Institute of Technology (SAIT) MaX Ferroelectricity machine learning Thermodynamics Dielectric response

Version history:

2022.88 (version v1) [This version] Jun 29, 2022 DOI10.24435/materialscloud:9g-k6