Artificial intelligence for materials science / Yuan Cheng, Tian Wang, Gang Zhang, editors.
2021
TA404.23
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Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
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Can lend chapters, not whole ebooks
Details
Title
Artificial intelligence for materials science / Yuan Cheng, Tian Wang, Gang Zhang, editors.
ISBN
3030683109 (electronic book)
9783030683115 (print)
3030683117
9783030683122 (print)
3030683125
9783030683108 (electronic bk.)
9783030683092
3030683095
9783030683115 (print)
3030683117
9783030683122 (print)
3030683125
9783030683108 (electronic bk.)
9783030683092
3030683095
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource (vii, 228 pages) : illustrations (some color)
Item Number
10.1007/978-3-030-68310-8 doi
Call Number
TA404.23
Dewey Decimal Classification
620.1/10285
Summary
Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed April 15, 2021).
Series
Springer series in materials science ; v. 312. 0933-033X
Available in Other Form
Print version: 9783030683092
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Online Access
Record Appears in
Online Resources > Ebooks
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All Resources
Table of Contents
Brief Introduction of the Machine Learning Method
Machine learning for high-entropy alloys
Two-way TrumpetNets and TubeNets for Identification of Material Parameters
Machine learning interatomic force fields for carbon allotropic materials
Genetic Algorithms
Accelerated Discovery of Thermoelectric Materials using Machine Learning
Thermal nanostructure design based on materials informatics
Machine Learning Accelerated Insights of Perovskite Materials.
Machine learning for high-entropy alloys
Two-way TrumpetNets and TubeNets for Identification of Material Parameters
Machine learning interatomic force fields for carbon allotropic materials
Genetic Algorithms
Accelerated Discovery of Thermoelectric Materials using Machine Learning
Thermal nanostructure design based on materials informatics
Machine Learning Accelerated Insights of Perovskite Materials.