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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 |
Files in This Item:
File | Description | Size | Format | |
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XIE_princeton_0181D_15162.pdf | 14.64 MB | Adobe PDF | View/Download |
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