國立虎尾科技大學 |

Machine Learning for Materials Discovery and Design.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning for Materials Discovery and Design./
Author:	Ward, Logan Timothy.
Description:	1 online resource (204 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Subject:	Materials science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369678864

Machine Learning for Materials Discovery and Design.
Ward, Logan Timothy.

Machine Learning for Materials Discovery and Design. - 1 online resource (204 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--Northwestern University, 2017.

Includes bibliographical references

The impacts of many important technologies are limited by the availability of better-performing materials. One factor limiting the ability of engineers to develop better materials is the speed at which they can search through possible formulations and processing schemes. Recently, machine learning algorithms have emerged as a possible route to reusing existing materials data to guide the design process. In this thesis, we discuss work towards addressing three major challenges in the use of machine learning in materials engineering. First, we implemented an automated toolkit for solving crystal structures and use it to improve the quality of an existing materials database. Second, we developed general-purpose methods for creating machine learning models from materials data, which will simplify and accelerate the development of new models. Third, we created open-source software for making these machine learning techniques more readily-accessible to the materials community. Along with addressing these challenges, we also demonstrate how machine learning can be applied to optimize existing and discover new Bulk Metallic Glass alloys. It is our vision that the methods developed in this work will help enable the application of machine learning to a wider variety of problems and, potentially, be used to improve materials employed in many different technologies.

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

Mode of access: World Wide Web

ISBN: 9781369678864Subjects--Topical Terms:

557839
Materials science.
Index Terms--Genre/Form:

554714
Electronic books.

Machine Learning for Materials Discovery and Design.
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100 1 $a Ward, Logan Timothy. $3 1187131
245 1 0 $a Machine Learning for Materials Discovery and Design.
264 0 $c 2017
300 $a 1 online resource (204 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Christopher Wolverton.
502 $a Thesis (Ph.D.)--Northwestern University, 2017.
504 $a Includes bibliographical references
520 $a The impacts of many important technologies are limited by the availability of better-performing materials. One factor limiting the ability of engineers to develop better materials is the speed at which they can search through possible formulations and processing schemes. Recently, machine learning algorithms have emerged as a possible route to reusing existing materials data to guide the design process. In this thesis, we discuss work towards addressing three major challenges in the use of machine learning in materials engineering. First, we implemented an automated toolkit for solving crystal structures and use it to improve the quality of an existing materials database. Second, we developed general-purpose methods for creating machine learning models from materials data, which will simplify and accelerate the development of new models. Third, we created open-source software for making these machine learning techniques more readily-accessible to the materials community. Along with addressing these challenges, we also demonstrate how machine learning can be applied to optimize existing and discover new Bulk Metallic Glass alloys. It is our vision that the methods developed in this work will help enable the application of machine learning to a wider variety of problems and, potentially, be used to improve materials employed in many different technologies.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Materials science. $3 557839
655 7 $a Electronic books. $2 local $3 554714
690 $a 0794
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Northwestern University. $b Materials Science and Engineering. $3 1181794
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10252569 $z click for full text (PQDT)