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Theoretical Modeling of Nanoscale Sy...

Zhou, Guoqing,

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Theoretical Modeling of Nanoscale Systems: Applications and Method Development /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Theoretical Modeling of Nanoscale Systems: Applications and Method Development // Guoqing Zhou.
作者:	Zhou, Guoqing,
面頁冊數:	1 electronic resource (164 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: B.
Contained By:	Dissertations Abstracts International84-04B.
標題:	Physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29390132
ISBN:	9798351448763

Theoretical Modeling of Nanoscale Systems: Applications and Method Development /
Zhou, Guoqing,

Theoretical Modeling of Nanoscale Systems: Applications and Method Development /Guoqing Zhou. - 1 electronic resource (164 pages)

Source: Dissertations Abstracts International, Volume: 84-04, Section: B.

Nanomaterials exhibit extraordinary thermal, mechanic, optical, electrical and magnetic properties and have wide applications in engineering, materials science and physical chemistry. The theoretical modeling of these nanoscale systems provides a fundamental understanding of various processes, such as physical and chemical synthesis, nuclei and electron dynamics, carrier and energy transfer. Based on the mechanism and timescale, the modeling of these processes requires the development and utilization of theories in classical and quantum dynamics, electronic structure. In this dissertation, two main methods, classical molecular dynamics and non-adiabatic molecular dynamics, will be presented in different scenarios with applied on studying the liquid phase exfoliation process in generating layered materials and the carrier dynamics in carbon nanotube and graphene nanoribbon. We detail the computational schemes along with accurate description for properties of interest. At the same time, we highlight their limitations and disadvantages and the contributions we make to improve their applicability. One contribution we make is to incorporate proper carrier interactions for modeling multi-particle processes with non-adiabatic molecular dynamics. Apart from this, due to the dense manifold of electronic degrees of freedom in nanoscale systems, the modeling requires efficient algorithms and software, and accurate analyzing tools. We incorporate techniques from machine learning into the theoretical modeling on nanoscale systems. We present an unsupervised learning technique for extracting implicit dependency of properties affecting nuclei and electronic dynamics and provides quantitative estimation and comparison of impact from lattice system on the electron recombination. In addition, we present a semi-empirical quantum chemistry model implemented with machine learning framework for improving efficiency and feasibility. The package is designed to take advantage of modern computer architecture graphic process units and is benchmarked with a set of organic molecules. Finally, we present a hybrid model with the developed package and supervised learning technique: artificial neural network. The hybrid model is developed to address the interpretability and transferability problem in machine learning when applying into material science. Incorporating the domain knowledge of quantum chemistry helps alleviate these issues and at the same time, machine learning greatly improves the accuracy of this semi-empirical model. The simplicity, efficiency and accuracy from this hybrid semi-empirical model allows the applications on large molecule systems for modeling long time processes and requiring quantum chemistry level accuracy. This model can be further extended to incorporate with non-adiabatic molecular dynamics for studying processes involving fast and rapid electron dynamics and to push forward the edges of theoretical modeling on nanoscale systems.

English

ISBN: 9798351448763Subjects--Topical Terms:

516296
Physics.
Subjects--Index Terms:

Non-adiabatic molecular dynamics

Theoretical Modeling of Nanoscale Systems: Applications and Method Development /
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520 $a Nanomaterials exhibit extraordinary thermal, mechanic, optical, electrical and magnetic properties and have wide applications in engineering, materials science and physical chemistry. The theoretical modeling of these nanoscale systems provides a fundamental understanding of various processes, such as physical and chemical synthesis, nuclei and electron dynamics, carrier and energy transfer. Based on the mechanism and timescale, the modeling of these processes requires the development and utilization of theories in classical and quantum dynamics, electronic structure. In this dissertation, two main methods, classical molecular dynamics and non-adiabatic molecular dynamics, will be presented in different scenarios with applied on studying the liquid phase exfoliation process in generating layered materials and the carrier dynamics in carbon nanotube and graphene nanoribbon. We detail the computational schemes along with accurate description for properties of interest. At the same time, we highlight their limitations and disadvantages and the contributions we make to improve their applicability. One contribution we make is to incorporate proper carrier interactions for modeling multi-particle processes with non-adiabatic molecular dynamics. Apart from this, due to the dense manifold of electronic degrees of freedom in nanoscale systems, the modeling requires efficient algorithms and software, and accurate analyzing tools. We incorporate techniques from machine learning into the theoretical modeling on nanoscale systems. We present an unsupervised learning technique for extracting implicit dependency of properties affecting nuclei and electronic dynamics and provides quantitative estimation and comparison of impact from lattice system on the electron recombination. In addition, we present a semi-empirical quantum chemistry model implemented with machine learning framework for improving efficiency and feasibility. The package is designed to take advantage of modern computer architecture graphic process units and is benchmarked with a set of organic molecules. Finally, we present a hybrid model with the developed package and supervised learning technique: artificial neural network. The hybrid model is developed to address the interpretability and transferability problem in machine learning when applying into material science. Incorporating the domain knowledge of quantum chemistry helps alleviate these issues and at the same time, machine learning greatly improves the accuracy of this semi-empirical model. The simplicity, efficiency and accuracy from this hybrid semi-empirical model allows the applications on large molecule systems for modeling long time processes and requiring quantum chemistry level accuracy. This model can be further extended to incorporate with non-adiabatic molecular dynamics for studying processes involving fast and rapid electron dynamics and to push forward the edges of theoretical modeling on nanoscale systems.
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650 4 $a Computational chemistry. $3 3350019
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653 $a Non-adiabatic molecular dynamics
653 $a Rapid electron dynamics
653 $a Electron recombination
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710 2 $a University of Southern California. $b Physics. $e degree granting institution. $3 3816203
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