東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Optical Property Prediction and Mole...

Greenman, Kevin P.,

FindBook

Google Book

Amazon

博客來

Optical Property Prediction and Molecular Discovery Through Multi-Fidelity Deep Learning and Computational Chemistry /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optical Property Prediction and Molecular Discovery Through Multi-Fidelity Deep Learning and Computational Chemistry // Kevin P Greenman.
作者:	Greenman, Kevin P.,
面頁冊數:	1 electronic resource (201 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
標題:	Physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31851001
ISBN:	9798304953580

Optical Property Prediction and Molecular Discovery Through Multi-Fidelity Deep Learning and Computational Chemistry /
Greenman, Kevin P.,

Optical Property Prediction and Molecular Discovery Through Multi-Fidelity Deep Learning and Computational Chemistry /Kevin P Greenman. - 1 electronic resource (201 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: B.

Optical properties are crucial for the design of molecules for numerous applications, including for display technologies and biological imaging. The accurate prediction of these properties has been the subject of decades of work in both physics-based approaches and statistical modeling. Recently, large datasets of both computed and experimental optical properties have become available, along with the advent of powerful deep learning approaches cable of learning meaningful representations from these large datasets. This thesis presents new approaches for predicting optical properties by fusing the experimental and computational data in multi-fidelity models that achieve greater accuracy and generalizability than previous methods. Additionally, it conducts a thorough benchmark of various strategies for handling multi-fidelity data to inform the modeling choices of future practitioners working with optical properties and beyond. Despite the greater availability of optical property data recently, the near-infrared (NIR) region of the spectrum remains more data-sparse despite its promise in many applications. This thesis demonstrates the shortcomings of existing methods for predicting optical properties in this region of chemical space and recommends best practices for future research in this area. Finally, this thesis highlights successful usage of data-driven optical property prediction for the discovery of novel molecules for specific applications.

English

ISBN: 9798304953580Subjects--Topical Terms:

516296
Physics.
Subjects--Index Terms:

Physics-based approaches

Optical Property Prediction and Molecular Discovery Through Multi-Fidelity Deep Learning and Computational Chemistry /
LDR:02960nmm a22004333i 4500 001 2438285
005 20251210114000.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798304953580
035 $a (MiAaPQD)AAI31851001
035 $a (MiAaPQD)MIT17211155385
035 $a AAI31851001
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Greenman, Kevin P., $e author. $3 3829538
245 1 0 $a Optical Property Prediction and Molecular Discovery Through Multi-Fidelity Deep Learning and Computational Chemistry / $c Kevin P Greenman.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (201 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: 86-08, Section: B.
500 $a Advisors: Gomez-Bombarelli, Rafael; Green, William H.
502 $b Ph.D. $c Massachusetts Institute of Technology $d 2024.
520 $a Optical properties are crucial for the design of molecules for numerous applications, including for display technologies and biological imaging. The accurate prediction of these properties has been the subject of decades of work in both physics-based approaches and statistical modeling. Recently, large datasets of both computed and experimental optical properties have become available, along with the advent of powerful deep learning approaches cable of learning meaningful representations from these large datasets. This thesis presents new approaches for predicting optical properties by fusing the experimental and computational data in multi-fidelity models that achieve greater accuracy and generalizability than previous methods. Additionally, it conducts a thorough benchmark of various strategies for handling multi-fidelity data to inform the modeling choices of future practitioners working with optical properties and beyond. Despite the greater availability of optical property data recently, the near-infrared (NIR) region of the spectrum remains more data-sparse despite its promise in many applications. This thesis demonstrates the shortcomings of existing methods for predicting optical properties in this region of chemical space and recommends best practices for future research in this area. Finally, this thesis highlights successful usage of data-driven optical property prediction for the discovery of novel molecules for specific applications.
546 $a English
590 $a School code: 0753
650 4 $a Physics. $3 516296
650 4 $a Deep learning. $3 3554982
650 4 $a Chemistry. $3 516420
650 4 $a Role models. $3 3301554
650 4 $a Chemical engineering. $3 560457
653 $a Physics-based approaches
653 $a Deep learning
690 $a 0485
690 $a 0542
690 $a 0800
690 $a 0605
710 2 $a Massachusetts Institute of Technology. $b Department of Chemical Engineering. $e degree granting institution. $3 3823497
720 1 $a Gomez-Bombarelli, Rafael $e degree supervisor.
720 1 $a Green, William H. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-08B.
790 $a 0753
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31851001