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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01f1881q28h
Title: Ab initio multi-scale modeling of crystals: methods and applications in ferroelectrics
Authors: XIE, PINCHEN
Advisors: Car, Roberto
E, Weinan
Contributors: Applied and Computational Mathematics Department
Subjects: Applied mathematics
Physics
Chemistry
Issue Date: 2024
Publisher: Princeton, NJ : Princeton University
Abstract: The ab initio density functional theory (DFT), all-atom molecular dynamics (MD), and coarse-grained dynamics are effective physical models bridging the microscale with the mesoscale. The Born-Oppenheimer approximation and the Mori-Zwanzig formalism indicate the conceptual consistency among these models. However, in multi-scale physical modeling, the numerical consistency among these models is still a long-term pursuit. Machine learning addresses this issue by parameterizing a coarse-grain model with data provided by a fine-grain model. We apply the data-driven approach to the multi-scale modeling of crystalline material and use ferroelectrics for demonstration. We use machine-learned potential energy surface and polarization surface to bridge DFT and all-atom MD. Then, we propose a machine-learned generalized Langevin equation to bridge all-atom MD and coarse-grained lattice dynamics. Consistency on static and dynamical material properties is demonstrated for the prototypical ferroelectric material lead titanate by modeling its paraelectric-ferroelectric phase transition and domain motion. The methodologies described can be readily applied to a lot of other crystals.
URI: http://arks.princeton.edu/ark:/88435/dsp01f1881q28h
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Applied and Computational Mathematics

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