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Computational Tools for Chemical Rea...

University of California, Irvine.

Computational Tools for Chemical Reactions : = Simulation & Prediction.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Computational Tools for Chemical Reactions :/
其他題名:	Simulation & Prediction.
作者:	Fooshee, David.
面頁冊數:	1 online resource (106 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355413984

Computational Tools for Chemical Reactions : = Simulation & Prediction.
Fooshee, David.

Computational Tools for Chemical Reactions :Simulation & Prediction. - 1 online resource (106 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2017.

Includes bibliographical references

Achieving human-level performance at predicting chemical reactions remains an open prob- lem with broad potential applications. Here we describe a deep learning-based tool for chemical reaction prediction and product identification. Significant efforts were made to curate and refine a new, high-quality data set of hand-selected chemical reactions written at the level of elementary electron movements. Using deep artificial neural networks trained on this data, we demonstrate a high degree of accuracy at predicting real-world reactions. Because predictions are made at the elementary step level, they can be chained together to form multi-step reaction pathway searches, to help identify unknown side products.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355413984Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Computational Tools for Chemical Reactions : = Simulation & Prediction.
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100 1 $a Fooshee, David. $3 1184418
245 1 0 $a Computational Tools for Chemical Reactions : $b Simulation & Prediction.
264 0 $c 2017
300 $a 1 online resource (106 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Pierre F. Baldi.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2017.
504 $a Includes bibliographical references
520 $a Achieving human-level performance at predicting chemical reactions remains an open prob- lem with broad potential applications. Here we describe a deep learning-based tool for chemical reaction prediction and product identification. Significant efforts were made to curate and refine a new, high-quality data set of hand-selected chemical reactions written at the level of elementary electron movements. Using deep artificial neural networks trained on this data, we demonstrate a high degree of accuracy at predicting real-world reactions. Because predictions are made at the elementary step level, they can be chained together to form multi-step reaction pathway searches, to help identify unknown side products.
520 $a We also present a computational brewing application, COBRA, capable of simulating com- plex chemical mixtures. We demonstrate its efficacy at modeling both the photooxidation of isoprene, and the oxidation of squalene in the presence of ozone, by comparing predicted results with results obtained from high-resolution mass spectrometry.
520 $a In addition, we address the problem of atom-mapping for chemical reactions, by designing a new atom-mapping algorithm that can be used to annotate unmapped reactions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Chemistry. $3 593913
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0485
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Irvine. $b Computer Science. $3 1179555
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10634473 $z click for full text (PQDT)

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