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Machine Intelligence for Chemistry: ...

Tavakoli, Mohammadamin.

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Machine Intelligence for Chemistry: From Deep Learning Architectures to Open Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Intelligence for Chemistry: From Deep Learning Architectures to Open Data./
Author:	Tavakoli, Mohammadamin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	111 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
Subject:	Atmospheric chemistry. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30640684
ISBN:	9798380858847

Machine Intelligence for Chemistry: From Deep Learning Architectures to Open Data.
Tavakoli, Mohammadamin.

Machine Intelligence for Chemistry: From Deep Learning Architectures to Open Data. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 111 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--University of California, Irvine, 2023.

This item must not be sold to any third party vendors.

Achieving human-expert performance in predicting the outcomes of chemical reactions is a major open challenge in AI and chemistry. A solution to this challenge would have significant practical applications in areas ranging from drug design to atmospheric chemistry. However, in order to address this challenge, many issues need to be overcome including the lack of open data, the combinatorial and physical complexity of chemical reactions, and the need for interpretable solutions that illuminate the underlying reaction mechanisms. We will describe three projects aimed at addressing these challenges including the development and deployment of public databases of chemical reaction steps, and the development and training of deep graph neural network and transformer architectures to predict reaction outcomes in interpretable ways.

ISBN: 9798380858847Subjects--Topical Terms:

544140
Atmospheric chemistry.
Subjects--Index Terms:

Physical complexity

Machine Intelligence for Chemistry: From Deep Learning Architectures to Open Data.
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500 $a Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
500 $a Advisor: Baldi, Pierre.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Achieving human-expert performance in predicting the outcomes of chemical reactions is a major open challenge in AI and chemistry. A solution to this challenge would have significant practical applications in areas ranging from drug design to atmospheric chemistry. However, in order to address this challenge, many issues need to be overcome including the lack of open data, the combinatorial and physical complexity of chemical reactions, and the need for interpretable solutions that illuminate the underlying reaction mechanisms. We will describe three projects aimed at addressing these challenges including the development and deployment of public databases of chemical reaction steps, and the development and training of deep graph neural network and transformer architectures to predict reaction outcomes in interpretable ways.
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650 4 $a Computer science. $3 523869
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653 $a Graph neural network
653 $a Drug design
653 $a Chemical reactions
653 $a Transformer architectures
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710 2 $a University of California, Irvine. $b Computer Science. $3 2099759
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