Overview
- Presents fundamental information about AI principles and algorithms
- Describes the most important and commonly adopted analytical methods in computational material science
- Features applications of machine learning in material design
- Includes applications of these functional materials in various fields, from electronics, optoelectronics, spintronics, and thermoelectric energy conversion, to rechargeable ion batteries, solar cells, and robotics
Part of the book series: Springer Series in Materials Science (SSMATERIALS, volume 312)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Editors and Affiliations
About the editors
Dr. Tian Wang. Dr. Wangreceived the M.S. and Ph.D. degrees from Xi'an Jiaotong University, China, and the University of Technology of Troyes, France, in 2010 and 2014, respectively. He is currently an Associate Professor with the School of Automation of Science and Electrical Engineering, Beihang University. His research interests include artificial intelligence and machine learning.
Dr. Gang Zhang. Dr. Zhang received B. Sci and PhD in physics from Tsinghua University in 1998 and 2002, respectively. Prior to his joining Institute of High Performance Computing (IHPC), he was a professor at Department of Electronics, Peking University. His research focuses on electronic, thermal, and optical properties of novel materials and structures in important engineering problems, aims to develop a fundamental understanding of the processes underlying new technologies and to establish simulations tools for material and device design.
Bibliographic Information
Book Title: Artificial Intelligence for Materials Science
Editors: Yuan Cheng, Tian Wang, Gang Zhang
Series Title: Springer Series in Materials Science
DOI: https://doi.org/10.1007/978-3-030-68310-8
Publisher: Springer Cham
eBook Packages: Chemistry and Materials Science, Chemistry and Material Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-68309-2Published: 27 March 2021
Softcover ISBN: 978-3-030-68312-2Published: 29 March 2022
eBook ISBN: 978-3-030-68310-8Published: 26 March 2021
Series ISSN: 0933-033X
Series E-ISSN: 2196-2812
Edition Number: 1
Number of Pages: VII, 228
Number of Illustrations: 6 b/w illustrations, 101 illustrations in colour
Topics: Materials Science, general, Machine Learning, Materials Engineering