國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Intelligence for Chemistry :/
其他題名:	From Deep Learning Architectures to Open Data.
作者:	Tavakoli, Mohammadamin.
面頁冊數:	1 online resource (111 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
標題:	Atmospheric chemistry. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (111 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380858847Subjects--Topical Terms:

639403
Atmospheric chemistry.
Subjects--Index Terms:

Physical complexityIndex Terms--Genre/Form:

554714
Electronic books.

Machine Intelligence for Chemistry : = From Deep Learning Architectures to Open Data.
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653 $a Drug design
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653 $a Transformer architectures
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