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Local structural signature of cooperative dynamics in glassy liquid system

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Title
Local structural signature of cooperative dynamics in glassy liquid system
Author
Lee, Hyun Taek
Advisor
Kim, Jaeup
Keywords
Glassy liquids; Excitation; Soft domains; Machine learning
Issue Date
2020-08
Publisher
Graduate School of UNIST
Abstract
The rapid increase of the computing power and the development of the efficient machine learning techniques in recent days allow us to apply the machine learning to various field of researches. The machine learning techniques become effective solutions to the fields which are not well described by one integrated theory. The physics for the super cooled glassy liquid system is one such field. The prediction of local dynamics based on the local structural information is longstanding problem in glass physics. The latest researches reveal that the local dynamics is actually predictable by utilizing the support vector machine (SVM) and the local structural information. In this thesis, I reproduce the result of the former researches which utilize the SVM and compare it with the result of the neural network. In addition, I suggest the origin of the dynamic heterogeneity in glassy liquid system. Furthermore, I demonstrate the composition of the local soft regions in glassy liquid systems.
Description
Department of Physics
URI
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PHY_Theses_Ph.D.
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