東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Investigations into Proton Transfer ...

Secor, Maxim,

Linked to FindBook

Google Book

Amazon

博客來

Investigations into Proton Transfer and Energy Conversion Using Machine Learning and Quantum Chemistry /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Investigations into Proton Transfer and Energy Conversion Using Machine Learning and Quantum Chemistry // Maxim Secor.
Author:	Secor, Maxim,
Description:	1 electronic resource (334 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
Subject:	Chemistry. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30312671
ISBN:	9798379783822

Investigations into Proton Transfer and Energy Conversion Using Machine Learning and Quantum Chemistry /
Secor, Maxim,

Investigations into Proton Transfer and Energy Conversion Using Machine Learning and Quantum Chemistry /Maxim Secor. - 1 electronic resource (334 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Fossil fuels are a primary source of energy world-wide. These fuels will eventually be exhausted and have a deleterious effect on the environment through carbon dioxide emissions. Renewable sources of energy and more efficient electrochemical energy conversion technologies will be necessary to combat our reliance on fossil fuels. My dissertation studies proton transfer and catalysis with machine learning and quantum chemistry with ramifications for renewable energy and energy conversion. Machine learning models were trained to predict the time-independent and time-dependent quantum-mechanical properties of protons. This demonstrated the efficacy of machine learning in predicting nuclear quantum effects such as tunneling and zero-point energy. Catalysis was also studied using machine learning, and a featurization scheme was developed to screen metal complexes for catalysis by using their electronic density matrices to predict binding free energies. Proton transfer was also studied using density functional theory calculations to obtain redox potentials, IR spectra, IRSEC, and many other properties of biomimetic systems designed to model a proton transfer interface in Photosystem II with implications for the development of artificial photosynthesis.

English

ISBN: 9798379783822Subjects--Topical Terms:

516420
Chemistry.
Subjects--Index Terms:

Catalysis

Investigations into Proton Transfer and Energy Conversion Using Machine Learning and Quantum Chemistry /
LDR:02719nmm a22004333i 4500 001 2400423
005 20250522084123.5
006 m o d
007 cr|nu||||||||
008 251215s2023 miu||||||m |||||||eng d
020 $a 9798379783822
035 $a (MiAaPQD)AAI30312671
035 $a AAI30312671
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Secor, Maxim, $e author. $3 3770407
245 1 0 $a Investigations into Proton Transfer and Energy Conversion Using Machine Learning and Quantum Chemistry / $c Maxim Secor.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (334 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: 85-01, Section: B.
500 $a Advisors: Hammes-Schiffer, Sharon Committee members: Batista, Victor; Johnson, Mark.
502 $b Ph.D. $c Yale University $d 2023.
520 $a Fossil fuels are a primary source of energy world-wide. These fuels will eventually be exhausted and have a deleterious effect on the environment through carbon dioxide emissions. Renewable sources of energy and more efficient electrochemical energy conversion technologies will be necessary to combat our reliance on fossil fuels. My dissertation studies proton transfer and catalysis with machine learning and quantum chemistry with ramifications for renewable energy and energy conversion. Machine learning models were trained to predict the time-independent and time-dependent quantum-mechanical properties of protons. This demonstrated the efficacy of machine learning in predicting nuclear quantum effects such as tunneling and zero-point energy. Catalysis was also studied using machine learning, and a featurization scheme was developed to screen metal complexes for catalysis by using their electronic density matrices to predict binding free energies. Proton transfer was also studied using density functional theory calculations to obtain redox potentials, IR spectra, IRSEC, and many other properties of biomimetic systems designed to model a proton transfer interface in Photosystem II with implications for the development of artificial photosynthesis.
546 $a English
590 $a School code: 0265
650 4 $a Chemistry. $3 516420
650 4 $a Computational chemistry. $3 3350019
653 $a Catalysis
653 $a Machine learning
653 $a Proton transfer
653 $a Quantum chemistry
653 $a Energy conversion
690 $a 0485
690 $a 0219
690 $a 0800
710 2 $a Yale University. $b Chemistry. $e degree granting institution. $3 3770408
720 1 $a Hammes-Schiffer, Sharon $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-01B.
790 $a 0265
791 $a Ph.D.
792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30312671