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

(Co-)Crystal Structure Prediction Wi...

O'Connor, Dana.

FindBook

Google Book

Amazon

博客來

(Co-)Crystal Structure Prediction With Machine Learned Potentials.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	(Co-)Crystal Structure Prediction With Machine Learned Potentials./
作者:	O'Connor, Dana.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	150 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Computational chemistry. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30688503
ISBN:	9798380588812

(Co-)Crystal Structure Prediction With Machine Learned Potentials.
O'Connor, Dana.

(Co-)Crystal Structure Prediction With Machine Learned Potentials. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 150 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2023.

Molecular crystals are a class of materials that are held together by weak van der Waals interactions that have applications in pharmaceuticals, organic electronics, and energetic materials. Energetic materials (EMs) include explosives, propellants, and pyrotechnics. Because experimental screening methods can be costly, and in some cases hazardous, computational screening methods such as crystal structure prediction are crucial to the development of the field. Co-crystals are of particular interest in materials discovery and development due to the possibility of enhancing desirable properties from one or more of the co-formers while discarding less desirable properties. Due to the enormous search space inherent in co-crystal structure prediction, it is imperative to develop search algorithms capable of both exploration of the potential energy surface and exploitation of low energy regions. The random structure generator, Genarris, and the genetic algorithm, GAtor, have been shown to successfully predict the structure of single component crystal structures,including several EMs. These two algorithms have been adapted to search for co-crystals. Another component of co-crystal and crystal structure prediction is the computational expense to optimize and rank large systems. This is a rate limiting factor in co-crystal structure prediction. Hence, there has been interest in developing and using machine learned interatomic potentials, such as AIMNet, in crystal structure prediction workflows. We will benchmark the performance of AIMNet in comparison to density functional theory as well as use a trained AIMNet potential to perform CSP for a target of the most recent CCDC blind test.

ISBN: 9798380588812Subjects--Topical Terms:

3350019
Computational chemistry.
Subjects--Index Terms:

Crystal structure prediction

(Co-)Crystal Structure Prediction With Machine Learned Potentials.
LDR:02845nmm a2200373 4500 001 2403614
005 20241118135849.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380588812
035 $a (MiAaPQ)AAI30688503
035 $a AAI30688503
040 $a MiAaPQ $c MiAaPQ
100 1 $a O'Connor, Dana. $3 3773885
245 1 0 $a (Co-)Crystal Structure Prediction With Machine Learned Potentials.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 150 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
500 $a Advisor: Marom, Noa.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2023.
520 $a Molecular crystals are a class of materials that are held together by weak van der Waals interactions that have applications in pharmaceuticals, organic electronics, and energetic materials. Energetic materials (EMs) include explosives, propellants, and pyrotechnics. Because experimental screening methods can be costly, and in some cases hazardous, computational screening methods such as crystal structure prediction are crucial to the development of the field. Co-crystals are of particular interest in materials discovery and development due to the possibility of enhancing desirable properties from one or more of the co-formers while discarding less desirable properties. Due to the enormous search space inherent in co-crystal structure prediction, it is imperative to develop search algorithms capable of both exploration of the potential energy surface and exploitation of low energy regions. The random structure generator, Genarris, and the genetic algorithm, GAtor, have been shown to successfully predict the structure of single component crystal structures,including several EMs. These two algorithms have been adapted to search for co-crystals. Another component of co-crystal and crystal structure prediction is the computational expense to optimize and rank large systems. This is a rate limiting factor in co-crystal structure prediction. Hence, there has been interest in developing and using machine learned interatomic potentials, such as AIMNet, in crystal structure prediction workflows. We will benchmark the performance of AIMNet in comparison to density functional theory as well as use a trained AIMNet potential to perform CSP for a target of the most recent CCDC blind test.
590 $a School code: 0041.
650 4 $a Computational chemistry. $3 3350019
650 4 $a Materials science. $3 543314
650 4 $a Engineering. $3 586835
653 $a Crystal structure prediction
653 $a Density functional theory
653 $a Energetic materials
653 $a Machine learned potentials
690 $a 0794
690 $a 0219
690 $a 0537
710 2 $a Carnegie Mellon University. $b Materials Science and Engineering. $3 3281156
773 0 $t Dissertations Abstracts International $g 85-04B.
790 $a 0041
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30688503