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 t...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(BaTiO_(3)),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 BaTiO_(3)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.展开更多
基金L.G.,M.K.and M.C.were supported by the Samsung Advanced Institute of Technology(SAIT)M.V.,G.P.,N.M.and M.C.acknowledge support from the MARVEL National Centre of Competence in Research(NCCR),funded by the Swiss National Science Foundation(grant agreement ID 51NF40-182892)+2 种基金G.P.acknowledges the swissuniversities“Materials Cloud”project(number 201-003)G.P.and N.M.acknowledge support from the European Centre of Excellence MaX“Materials design at the Exascale”(824143)This work was supported by a grant from the Swiss National Supercomputing Centre(CSCS)under project IDs mr0 and s1073.
文摘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(BaTiO_(3)),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 BaTiO_(3)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.
