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

FindBook

Google Book

Amazon

博客來

Accelerating Molecular Materials Discovery Following Data-Driven Approaches.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Accelerating Molecular Materials Discovery Following Data-Driven Approaches./
作者:	Vriza, Aikaterini.
面頁冊數:	1 online resource (220 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Databases. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29252081click for full text (PQDT)
ISBN:	9798841563549

Accelerating Molecular Materials Discovery Following Data-Driven Approaches.
Vriza, Aikaterini.

Accelerating Molecular Materials Discovery Following Data-Driven Approaches. - 1 online resource (220 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Thesis (Ph.D.)--The University of Liverpool (United Kingdom), 2022.

Includes bibliographical references

Designing new materials with desired properties is one of the main challenges for the current industrial and academic research, in the attempt to cover the societal demands. The 'utopia' would be, not only to find more reliable methodologies but also develop smarter ways for accelerating their discovery. Data-driven approaches are gaining ground as a tool for detecting patterns in known datasets and perform straightforward predictions.The first Chapter provides a wide overview of the developmentsthe data science tools have brought to the molecular world in the past years and covers the main theoretical aspects of the studied materials.In Chapter two, a broad overview of the methods used to support this work is given covering both data science and computational chemistry aspects.Chapter three is about the study of the relations between electronic properties and molecular structure in polyaromatic hydrocarbons, which are the building blocks of the materials studied herein.Chapter four is diving more into the metal-polyaromatic hydrocarbon systems, starting from the extraction of all the available information regarding the currently known systems and further on developing strategies on how to guide the selection of the next most interesting systems.Chapters five and six are related to co-crystals and how machine learning can be effectively used to provide an insilico screening tool to prioritize molecular pairs that have high probability to form co-crystals. Chapter five is focused on the formation of molecular crystals, that consist of two components (co-crystals) connected via π-π interactions that might have electronic functionalities, i.e., conductivity, whereas in chapter six the methodology developed for π-π co-crystals is scaled-up to cover all the co-crystal types. In both chapters, computational and machine learning approaches are implemented to detect promising coformers. Cambridge Structural Database (CSD), which is the world's repository for small-molecule organic crystal structures is the knowledge source for extracting the crystal structures of interest and then trying to understand the rules that guide their existence in terms of their conformer combinations.Overall, this work is an attempt to combine predictive approaches using various machine learning algorithms with high-throughput computational modelling to guide the synthesis of new functional organic crystals. It is postulated that the complementarity of these tools will enable us to gain better insight into the materials discovery problems and thus drive to innovative and creative solutions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841563549Subjects--Topical Terms:

747532
Databases.
Index Terms--Genre/Form:

542853
Electronic books.

Accelerating Molecular Materials Discovery Following Data-Driven Approaches.
LDR:03889nmm a2200325K 4500 001 2355478
005 20230512095942.5
006 m o d
007 cr mn ---uuuuu
008 241011s2022 xx obm 000 0 eng d
020 $a 9798841563549
035 $a (MiAaPQ)AAI29252081
035 $a (MiAaPQ)Liverpool_3153447
035 $a AAI29252081
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Vriza, Aikaterini. $3 3695904
245 1 0 $a Accelerating Molecular Materials Discovery Following Data-Driven Approaches.
264 0 $c 2022
300 $a 1 online resource (220 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: 84-02, Section: B.
500 $a Advisor: Dyer, Matthew ; Kurlin, Vitaliy ; Antypov, Dmytro ; Wood, Pete.
502 $a Thesis (Ph.D.)--The University of Liverpool (United Kingdom), 2022.
504 $a Includes bibliographical references
520 $a Designing new materials with desired properties is one of the main challenges for the current industrial and academic research, in the attempt to cover the societal demands. The 'utopia' would be, not only to find more reliable methodologies but also develop smarter ways for accelerating their discovery. Data-driven approaches are gaining ground as a tool for detecting patterns in known datasets and perform straightforward predictions.The first Chapter provides a wide overview of the developmentsthe data science tools have brought to the molecular world in the past years and covers the main theoretical aspects of the studied materials.In Chapter two, a broad overview of the methods used to support this work is given covering both data science and computational chemistry aspects.Chapter three is about the study of the relations between electronic properties and molecular structure in polyaromatic hydrocarbons, which are the building blocks of the materials studied herein.Chapter four is diving more into the metal-polyaromatic hydrocarbon systems, starting from the extraction of all the available information regarding the currently known systems and further on developing strategies on how to guide the selection of the next most interesting systems.Chapters five and six are related to co-crystals and how machine learning can be effectively used to provide an insilico screening tool to prioritize molecular pairs that have high probability to form co-crystals. Chapter five is focused on the formation of molecular crystals, that consist of two components (co-crystals) connected via π-π interactions that might have electronic functionalities, i.e., conductivity, whereas in chapter six the methodology developed for π-π co-crystals is scaled-up to cover all the co-crystal types. In both chapters, computational and machine learning approaches are implemented to detect promising coformers. Cambridge Structural Database (CSD), which is the world's repository for small-molecule organic crystal structures is the knowledge source for extracting the crystal structures of interest and then trying to understand the rules that guide their existence in terms of their conformer combinations.Overall, this work is an attempt to combine predictive approaches using various machine learning algorithms with high-throughput computational modelling to guide the synthesis of new functional organic crystals. It is postulated that the complementarity of these tools will enable us to gain better insight into the materials discovery problems and thus drive to innovative and creative solutions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Databases. $3 747532
650 4 $a Neural networks. $3 677449
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Liverpool (United Kingdom). $3 1684840
773 0 $t Dissertations Abstracts International $g 84-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29252081 $z click for full text (PQDT)