자료유형  

 학위논문

Control Number  

0017164559

International Standard Book Number  

9798384045847

Dewey Decimal Classification Number  

541

Main Entry-Personal Name  

Chen, Yuting.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Michigan., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

78 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Zgid, Dominika Kamila.

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2024.

Summary, Etc.  

요약Accurate treatment of electron correlation is crucial for computational and theoretical chemists as it influences reaction mechanisms, structural properties, and various spectroscopic quantities. Traditionally, the chemistry community has relied on Density Functional Theory (DFT) and wavefunction-based methods to address the problem of electron correlation. While relatively successful, these methods have limitations either in computational scalability or in the degree of electron correlation described.Green's functions provide an alternative formalism to study electron correlation. This thesis focuses on the self-consistent GW (scGW) approximation, which has been shown to describe a higher degree of electron correlation at a cost comparable to other more traditional wavefunction and Density Functional Theory methods.In Chapter 2 of this thesis, we show a way to account for finite size effects in periodic systems and demonstrate its applications to band structure diagrams. This work also demonstrates that Fock and Self-Energy matrix quantities are able to be extrapolated to the thermodynamic limit.In Chapter 3, this thesis presents a novel application of using Green's functions to train a DFT exchange-correlation functional that recovers the more strongly correlated scGW result at the cheaper DFT cost. It is found that the machine-learning-trained functional outperformed manually created functionals, showcasing the applicability of this approach. This work also showcases the limitations of machine learning by training just on scGW energies instead of enforcing exact conditions, as we find the lack of exact conditions causes the paramaterization to fail in regimes with fewer data points. These two works both focus on reproducing the accuracy of scGW calculations at lower computational costs.Chapter 4 presents an application of the self-consistent GW approximation to studying interaction energies in high-spin open-shell dimers. These systems have traditionally only been evaluated using DFT and wavefunction methods because it was believed scGW could not resolve the small quantity of interaction energies due to the usage of a numerical grid, and this work demonstrates that scGW is capable of studying these complex systems effectively while also highlighting some problems in the benchmark datasets that arise from using a restricted formalism compared to an unrestricted method.

Subject Added Entry-Topical Term  

Physical chemistry.

Subject Added Entry-Topical Term  

Physics.

Subject Added Entry-Topical Term  

Analytical chemistry.

Subject Added Entry-Topical Term  

Computational physics.

Subject Added Entry-Topical Term  

Computer science.

Index Term-Uncontrolled  

Electronic structure theory

Index Term-Uncontrolled  

Green's functions

Index Term-Uncontrolled  

Computational scalability

Index Term-Uncontrolled  

Self-consistent GW

Index Term-Uncontrolled  

Density Functional Theory

Added Entry-Corporate Name  

University of Michigan Chemistry

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:656804