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Building Models of Spectroscopy for ...

Chen, Michael Stephen.

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Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning./
作者:	Chen, Michael Stephen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	267 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Hydrocarbons. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30742176
ISBN:	9798381028027

Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning.
Chen, Michael Stephen.

Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 267 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Spectroscopic techniques provide us with a means of investigating a system's microscopic structure and dynamics. Accurate atomistic simulations can help us explicitly connect spectroscopic features to the underlying electronic and nuclear structure and dynamics that give rise to them. In this dissertation, I highlight my work in rendering accurate atomistic simulations of different linear and multidimensional spectroscopies more computationally tractable by leveraging semiclassical approaches for theoretically treating spectroscopies and developing machine learning (ML) models to serve as proxies for ab initio electronic structure calculations. Chapter 1 provides a quick overview of the ML approaches I employed and theoretical background for how I used molecular dynamics (MD) simulations to simulate different spectroscopies. Chapter 2 presents work I have conducted in training ML potential energy surfaces for liquid water using transfer learning to target high-level ab initio electronic structure theories in order to accurately and eciently conduct MD simulations. In Chapters 3 and 4, I develop ML models for electronic excitation energies in order to simulate linear and 2D electronic absorption spectroscopies for various solvated chromophore systems and highlight the inability of TDDFT to treat the extent to which hydrogen-bonding affects the distribution of excitation energies. Lastly, Chapter 5 highlights my work in developing a theoretical framework to simulate novel time-resolved X-ray diffraction experiments, which can be used to probe the orientational structural dynamics of disordered condensed phase systems, and benchmarking with results for liquid chloroform.

ISBN: 9798381028027Subjects--Topical Terms:

697428
Hydrocarbons.

Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning.
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