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Meeting MS&T22: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Author(s) Rajan Khadka, Pawel Keblinski
On-Site Speaker (Planned) Rajan Khadka
Abstract Scope We use molecular dynamics simulations to investigate the polarization switching dynamics in the single domain and 180o bidomain (i.e., including the preexisting domain wall) models in BaTiO3 and KNbO3. In a single domain study, for both materials, we observed that the hysteresis loop is essentially non-existent in the highest temperature non-cubic phase. We attribute this behavior to the observation of spontaneous local polarization fluctuations leading to the elimination of the nucleation barrier. Interestingly, in the case of the bidomain structure, while we observe domain migration driven by the electric field, at high fields new domain nucleation is severely suppressed by comparison with the single domain simulations. This behavior is explained by the suppression of simulation dimension fluctuations due to the presence of the two domains. We further demonstrate that artificial suppression of the simulation cell dimension fluctuations in the case of a single domain switching also suppresses new domain nucleation.

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D-8: Molecular Dynamic Simulations of Polymer Derived Ceramics
Data-driven Prediction of Room Temperature Density of Multicomponent Silicate-based Glasses
Data Driven Design and Enhancement of Machinable Glass Ceramics
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In-Silico Simulations of Polymer Pyrolysis
Machine Learning-Derived Atomistic Potentials for Y2Si2O7 and Yb2Si2O7
Machine Learning Defect Properties of Semiconductors
Machine Learning to Design and Discover Sustainable Cementitious Binders: Learning from Small Databases and Developing Closed-form Analytical Models
Molecular Dynamics Simulation of Tellurite Glasses
Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Natural Language Processing Aided Understanding of Material Science Literature
Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Predicting and Accessing Metastable Phases
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces

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