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Predicting the Young's Modulus of Si...

Yang, Kai.

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Predicting the Young's Modulus of Silicate Glasses by Molecular Dynamics Simulations and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting the Young's Modulus of Silicate Glasses by Molecular Dynamics Simulations and Machine Learning./
作者:	Yang, Kai.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	86 p.
附註:	Source: Masters Abstracts International, Volume: 81-12.
Contained By:	Masters Abstracts International81-12.
標題:	Civil engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28000859
ISBN:	9798641375540

Predicting the Young's Modulus of Silicate Glasses by Molecular Dynamics Simulations and Machine Learning.
Yang, Kai.

Predicting the Young's Modulus of Silicate Glasses by Molecular Dynamics Simulations and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 86 p.

Source: Masters Abstracts International, Volume: 81-12.

Thesis (M.S.)--University of California, Los Angeles, 2020.

This item must not be sold to any third party vendors.

Understanding the compositional dependence of properties of silicate glass is critical to design novel glasses for various technology applications. With the development in molecular dynamics simulations and machine learning techniques, a combined and fully computational approach, which is able to reveal the relationship between glass composition and its mechanical properties, can be developed and served as a guide prior to experiments and manufacturing. On one hand, machine learning is a powerful tool to predict the properties based on the existing database. On the other hand, molecular dynamics simulation cannot only produce sufficient data points for machine learning models but also provide a detailed picture of the atomic structure of glasses. This atomic-scale knowledge from molecular dynamics simulation contains an intrinsic relationship between glass compositions and their mechanical properties.Here, we first use molecular dynamics simulation to generate the dataset for calcium aluminosilicate glasses and apply different machine learning models to predict their Young's modulus using glass compositions in Chapter 1. Next, we apply topological constraint theory to quantify the atomic structures of simulated glasses and use this knowledge to predict Young's modulus for calcium aluminosilicate glass family in Chapter 2. Last, in Chapter 3, we propose a fully analytical model to link the network topology with glass compositions.

ISBN: 9798641375540Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

Machine learning

Predicting the Young's Modulus of Silicate Glasses by Molecular Dynamics Simulations and Machine Learning.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 86 p.
500 $a Source: Masters Abstracts International, Volume: 81-12.
500 $a Advisor: Bauchy, Mathieu.
502 $a Thesis (M.S.)--University of California, Los Angeles, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Understanding the compositional dependence of properties of silicate glass is critical to design novel glasses for various technology applications. With the development in molecular dynamics simulations and machine learning techniques, a combined and fully computational approach, which is able to reveal the relationship between glass composition and its mechanical properties, can be developed and served as a guide prior to experiments and manufacturing. On one hand, machine learning is a powerful tool to predict the properties based on the existing database. On the other hand, molecular dynamics simulation cannot only produce sufficient data points for machine learning models but also provide a detailed picture of the atomic structure of glasses. This atomic-scale knowledge from molecular dynamics simulation contains an intrinsic relationship between glass compositions and their mechanical properties.Here, we first use molecular dynamics simulation to generate the dataset for calcium aluminosilicate glasses and apply different machine learning models to predict their Young's modulus using glass compositions in Chapter 1. Next, we apply topological constraint theory to quantify the atomic structures of simulated glasses and use this knowledge to predict Young's modulus for calcium aluminosilicate glass family in Chapter 2. Last, in Chapter 3, we propose a fully analytical model to link the network topology with glass compositions.
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653 $a Topological constraints theory
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