Thermodynamics and dielectric response of BaTiO3 by data-driven modeling
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-09-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00845-0 |
| _version_ | 1797998251614404608 |
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| author | Lorenzo Gigli Max Veit Michele Kotiuga Giovanni Pizzi Nicola Marzari Michele Ceriotti |
| author_facet | Lorenzo Gigli Max Veit Michele Kotiuga Giovanni Pizzi Nicola Marzari Michele Ceriotti |
| author_sort | Lorenzo Gigli |
| collection | DOAJ |
| description | 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 (BaTiO3), a prototypical ferroelectric. The model uses ab initio calculations as a 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 BaTiO3 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 the dielectric response properties of the material in a full ab initio manner, again reproducing the correct qualitative experimental behavior. |
| first_indexed | 2024-04-11T10:45:41Z |
| format | Article |
| id | doaj.art-8cdc2c73d5bc4006a1b1172d42394223 |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-04-11T10:45:41Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-8cdc2c73d5bc4006a1b1172d423942232022-12-22T04:29:03ZengNature Portfolionpj Computational Materials2057-39602022-09-018111710.1038/s41524-022-00845-0Thermodynamics and dielectric response of BaTiO3 by data-driven modelingLorenzo Gigli0Max Veit1Michele Kotiuga2Giovanni Pizzi3Nicola Marzari4Michele Ceriotti5Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, École Polytechnique Fédérale de LausanneLaboratory of Computational Science and Modeling (COSMO), Institute of Materials, École Polytechnique Fédérale de LausanneTheory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de LausanneTheory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de LausanneTheory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de LausanneLaboratory of Computational Science and Modeling (COSMO), Institute of Materials, École Polytechnique Fédérale de LausanneAbstract 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 (BaTiO3), a prototypical ferroelectric. The model uses ab initio calculations as a 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 BaTiO3 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 the dielectric response properties of the material in a full ab initio manner, again reproducing the correct qualitative experimental behavior.https://doi.org/10.1038/s41524-022-00845-0 |
| spellingShingle | Lorenzo Gigli Max Veit Michele Kotiuga Giovanni Pizzi Nicola Marzari Michele Ceriotti Thermodynamics and dielectric response of BaTiO3 by data-driven modeling npj Computational Materials |
| title | Thermodynamics and dielectric response of BaTiO3 by data-driven modeling |
| title_full | Thermodynamics and dielectric response of BaTiO3 by data-driven modeling |
| title_fullStr | Thermodynamics and dielectric response of BaTiO3 by data-driven modeling |
| title_full_unstemmed | Thermodynamics and dielectric response of BaTiO3 by data-driven modeling |
| title_short | Thermodynamics and dielectric response of BaTiO3 by data-driven modeling |
| title_sort | thermodynamics and dielectric response of batio3 by data driven modeling |
| url | https://doi.org/10.1038/s41524-022-00845-0 |
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