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Towards understanding Big Data molec...

ProQuest Information and Learning Co.

Towards understanding Big Data molecular simulation using machine learning algorithms.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Towards understanding Big Data molecular simulation using machine learning algorithms./
Author:	Einakian, Mina.
Description:	1 online resource (77 pages)
Notes:	Source: Masters Abstracts International, Volume: 55-05.
Contained By:	Masters Abstracts International55-05(E).
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781339922805

Towards understanding Big Data molecular simulation using machine learning algorithms.
Einakian, Mina.

Towards understanding Big Data molecular simulation using machine learning algorithms. - 1 online resource (77 pages)

Source: Masters Abstracts International, Volume: 55-05.

Thesis (M.S.)

Includes bibliographical references

Analytical experiments and computer simulations have shown unusual thermal conductivity behavior of soft material under mechanical strain. At first, the conductivity decreases with the increase of the mechanical strain. After reaching a certain minima, with the application of additional strain, it starts increasing. While this behavior is confirmed via experiments as well as computer simulations using molecular modeling techniques, what causes this behavior is still considered an open question.

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

Mode of access: World Wide Web

ISBN: 9781339922805Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Towards understanding Big Data molecular simulation using machine learning algorithms.
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008 190606s2016 xx obm 000 0 eng d
020 $a 9781339922805
035 $a (MiAaPQ)AAI10133705
035 $a (MiAaPQ)iit:10320
035 $a AAI10133705
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Einakian, Mina. $3 1179313
245 1 0 $a Towards understanding Big Data molecular simulation using machine learning algorithms.
264 0 $c 2016
300 $a 1 online resource (77 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: Masters Abstracts International, Volume: 55-05.
500 $a Adviser: Ashfaq A. Khokhar.
502 $a Thesis (M.S.) $c Illinois Institute of Technology $d 2016.
504 $a Includes bibliographical references
520 $a Analytical experiments and computer simulations have shown unusual thermal conductivity behavior of soft material under mechanical strain. At first, the conductivity decreases with the increase of the mechanical strain. After reaching a certain minima, with the application of additional strain, it starts increasing. While this behavior is confirmed via experiments as well as computer simulations using molecular modeling techniques, what causes this behavior is still considered an open question.
520 $a This thesis studies the molecular simulations data from multiple simulations and investigates the use of machine learning techniques to understand the unusual thermal conductivity behavior of soft materials under mechanical strain. The data sets consists of terabytes of data with thousands of spatial as well temporal attributes. In particular, the influence of attributes related to molecular structure including bond length, bond angle, and dihedrals has been probed. Several machine learning and data mining techniques including clustering and association rule mining have been applied on different datasets with different thermal conductivity to find the relationship between the soft material molecular structure and thermal conductivity behavior. The results show that in the alkyls molecules the most important features that affect the thermal conductivity are the O-CH2 bond properties, especially when thermal conductivity is equal to 1.5+/-epsilon .
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Illinois Institute of Technology. $b Electrical and Computer Engineering. $3 1179314
773 0 $t Masters Abstracts International $g 55-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10133705 $z click for full text (PQDT)

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