Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

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{
  "metadata": {
    "edited_by": 576, 
    "owner": 691, 
    "_oai": {
      "id": "oai:materialscloud.org:2137"
    }, 
    "description": "The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. Therefore, one is often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT, to perform simulations that do not suffer from finite size effects. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, due to the presence of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations and the large size and time scales afforded by empirical models.", 
    "mcid": "2024.54", 
    "id": "2137", 
    "license": "Creative Commons Attribution 4.0 International", 
    "license_addendum": null, 
    "references": [
      {
        "citation": "L. Gigli, A. Goscinski, M. Ceriotti, G. A. Tribello, arXiv:2310.12579 [cond-mat.mtrl-sci]", 
        "type": "Preprint", 
        "url": "https://arxiv.org/abs/2310.12579", 
        "comment": "", 
        "doi": "https://doi.org/10.48550/arXiv.2310.12579"
      }
    ], 
    "doi": "10.24435/materialscloud:xw-g5", 
    "keywords": [
      "Ferroelectrics", 
      "Finite-size effects", 
      "Metadynamics", 
      "Phase transitions", 
      "Machine Learning potentials", 
      "hybrid-DFT ML", 
      "Dielectric correlations", 
      "MARVEL", 
      "SNSF", 
      "Sinergia"
    ], 
    "contributors": [
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Gigli", 
        "email": "cangelsi@hotmail.it", 
        "givennames": "Lorenzo"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Goscinski", 
        "email": "alex.goscinski@epfl.ch", 
        "givennames": "Alexander"
      }, 
      {
        "affiliations": [
          "Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland"
        ], 
        "familyname": "Ceriotti", 
        "email": "michele.ceriotti@epfl.ch", 
        "givennames": "Michele"
      }, 
      {
        "affiliations": [
          "Centre for Quantum Materials and Technologies (CQMT),\nSchool of Mathematics and Physics, Queen\u2019s University Belfast, Belfast, BT7 1NN"
        ], 
        "familyname": "Tribello", 
        "email": "g.tribello@qub.ac.uk", 
        "givennames": "Gareth A."
      }
    ], 
    "conceptrecid": "2136", 
    "version": 1, 
    "publication_date": "Apr 10, 2024, 12:28:25", 
    "is_last": true, 
    "status": "published", 
    "_files": [
      {
        "size": 48028893, 
        "checksum": "md5:eebb021feba3d4cf9234aa6da64a7ec1", 
        "description": "This MaterialsCloud archive provides the datasets and Machine Learning models needed to run the calculations and reproduce the results and figures in the manuscript \"Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling\", by L. Gigli, A. Goscinski, M. Ceriotti, G. A. Tribello, arXiv:2310.12579 [cond-mat.mtrl-sci], 2023.", 
        "key": "Large-BTO-MaterialsCloud.zip"
      }
    ], 
    "title": "Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling"
  }, 
  "id": "2137", 
  "updated": "2024-04-10T10:28:25.724278+00:00", 
  "created": "2024-04-07T18:57:36.679371+00:00", 
  "revision": 4
}