國立虎尾科技大學 |

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning./
Author:	Chen, Michael Stephen.
Description:	1 online resource (267 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Optics. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (267 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381028027Subjects--Topical Terms:

595336
Optics.
Index Terms--Genre/Form:

554714
Electronic books.

Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning.
LDR:03082ntm a22003857 4500 001 1150308
005 20241028114743.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381028027
035 $a (MiAaPQ)AAI30742176
035 $a (MiAaPQ)STANFORDjg553nr2101
035 $a AAI30742176
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Chen, Michael Stephen. $3 1476769
245 1 0 $a Building Models of Spectroscopy for Condensed Phase Systems with Atomistic Detail Using Theory and Machine Learning.
264 0 $c 2023
300 $a 1 online resource (267 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Markland, Thomas;Kanan, Matthew;Rotskoff, Grant.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Optics. $3 595336
650 4 $a Mathematics. $3 527692
650 4 $a Chemistry. $3 593913
650 4 $a Analytical chemistry. $3 1182118
650 4 $a Energy. $3 784773
650 4 $a Anisotropy. $3 744114
650 4 $a Decomposition. $3 1372547
650 4 $a Hydrogen. $3 672179
650 4 $a Neural networks. $3 1011215
650 4 $a Solvents. $3 891167
650 4 $a Fourier transforms. $3 1372561
650 4 $a Spectrum analysis. $3 582358
650 4 $a Hydrocarbons. $3 673437
655 7 $a Electronic books. $2 local $3 554714
690 $a 0791
690 $a 0486
690 $a 0800
690 $a 0485
690 $a 0405
690 $a 0752
710 2 $a Stanford University. $3 1184533
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 85-06B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30742176 $z click for full text (PQDT)