國立虎尾科技大學 |

Nano-Scale to Meso-Scale : = Practice and Pedagogy of Deep Learning Applied to Molecular Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Nano-Scale to Meso-Scale :/
Reminder of title:	Practice and Pedagogy of Deep Learning Applied to Molecular Systems.
Author:	Komp, Evan.
Description:	1 online resource (234 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: A.
Contained By:	Dissertations Abstracts International85-10A.
Subject:	Chemical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798382213682

Nano-Scale to Meso-Scale : = Practice and Pedagogy of Deep Learning Applied to Molecular Systems.
Komp, Evan.

Nano-Scale to Meso-Scale :Practice and Pedagogy of Deep Learning Applied to Molecular Systems. - 1 online resource (234 pages)

Source: Dissertations Abstracts International, Volume: 85-10, Section: A.

Thesis (Ph.D.)--University of Washington, 2024.

Includes bibliographical references

Deep learning has found its way into the chemical sciences, and brought with it successes that have been observed in "traditional" applications such as computer vision and sentiment analysis. The integration of these methods into modern industry, pharma, and materials discovery makes it clear that the next generation of chemical and biological scientists and engineers will be operating at the intersection of data science and their respective fields. Unfortunately, nuances in these chemical systems mean that often methods leveraged in computer science applications are not out-of-the box translatable; expert knowledge is required to design a machine learning solution at the intersection of these fields. Here, this nuanced design process is detailed for two deep learning applications to the prediction of reaction rate constants, critical for understanding and design of reactive systems. Additionally, an optional learning event, designed to give undergraduate students an opportunity to learn and apply modern methods at this intersection, is detailed. Finally, work to leverage deep neural machine translation to improve protein thermal stability is presented, and the nuances highlighted. The translator has the ability to produce thermally stable variants of existing proteins or score already generated variants, allowing them to be leveraged at high temperatures where many proteins lose their applicability compared to other materials.8.

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

Mode of access: World Wide Web

ISBN: 9798382213682Subjects--Topical Terms:

555952
Chemical engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Nano-Scale to Meso-Scale : = Practice and Pedagogy of Deep Learning Applied to Molecular Systems.
LDR:02821ntm a22003977 4500 001 1150140
005 20241022111557.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798382213682
035 $a (MiAaPQ)AAI30994134
035 $a AAI30994134
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Komp, Evan. $3 1476573
245 1 0 $a Nano-Scale to Meso-Scale : $b Practice and Pedagogy of Deep Learning Applied to Molecular Systems.
264 0 $c 2024
300 $a 1 online resource (234 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: 85-10, Section: A.
500 $a Advisor: Beck, David A. C.
502 $a Thesis (Ph.D.)--University of Washington, 2024.
504 $a Includes bibliographical references
520 $a Deep learning has found its way into the chemical sciences, and brought with it successes that have been observed in "traditional" applications such as computer vision and sentiment analysis. The integration of these methods into modern industry, pharma, and materials discovery makes it clear that the next generation of chemical and biological scientists and engineers will be operating at the intersection of data science and their respective fields. Unfortunately, nuances in these chemical systems mean that often methods leveraged in computer science applications are not out-of-the box translatable; expert knowledge is required to design a machine learning solution at the intersection of these fields. Here, this nuanced design process is detailed for two deep learning applications to the prediction of reaction rate constants, critical for understanding and design of reactive systems. Additionally, an optional learning event, designed to give undergraduate students an opportunity to learn and apply modern methods at this intersection, is detailed. Finally, work to leverage deep neural machine translation to improve protein thermal stability is presented, and the nuances highlighted. The translator has the ability to produce thermally stable variants of existing proteins or score already generated variants, allowing them to be leveraged at high temperatures where many proteins lose their applicability compared to other materials.8.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Chemical engineering. $3 555952
650 4 $a Bioinformatics. $3 583857
650 4 $a Pedagogy. $3 1148703
653 $a Deep learning
653 $a Kinetics
653 $a Proteins
653 $a Thermal stability
655 7 $a Electronic books. $2 local $3 554714
690 $a 0542
690 $a 0800
690 $a 0715
690 $a 0456
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Washington. $b Chemical Engineering. $3 1182409
773 0 $t Dissertations Abstracts International $g 85-10A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30994134 $z click for full text (PQDT)