Artificial Intelligence for Materials Science

Author(s)

• Cheng, Yuan [Herausgeber]
• Wang, Tian
• Zhang, Gang

Bibliographic Information

Artificial Intelligence for Materials Science

Yuan Cheng, Tian Wang, Gang Zhang, editors

（Springer series in materials science, 312）

Springer, c2021

Note

Includes bibliographical references and index

Description and Table of Contents

Description

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.

Table of Contents

• Chapter 1. Materials Genome Initiatives: Past, Present, and Prospect Gang Zhang, Institute of High Performance Computing, A*STAR, 138632 Singapore. [e-mail] Chapter 2. Introduction of the Machine Learning method Tian Wang, Hichem Snoussi 1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China. Email: [e-mail] 2. Institute Charles Delaunay-LM2S FRE CNRS 2019, University of Technology of Troyes, Troyes 10030, France. Email: [e-mail] Chapter 3. Machine learning for high entropy alloys Yuan Cheng, Huajian Gao Institute of High Performance Computing, A*STAR, 138632 Singapore
• School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 70 Nanyang Drive, Singapore 637457, Singapore. [e-mail] Chapter 4. Machine learning for biomaterial design Markus J. Buehler, Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 1-290, Cambridge, Massachusetts 02139, USA. Email: [e-mail] Chapter 5. Rapid Photovoltaic Device Characterization through AI technology Tonio Buonassisi, MIT. Email: [e-mail] Chapter 6. Machine learning for thermal contact design Junichiro Shiomi, The University of Tokyo, Japan. [e-mail] Chapter 7. Discovery of new thermoelectric material through high-throughput calculation Wenqing Zhang, Southern University of Science and Technology, China. [e-mail] Chapter 8. Machine learning for high heat conductive material Eric S. Toberer, Colorado School of Mines, USA. E-mail: [e-mail] Chapter 9. Machine learning assisted discovery of new 2D Materials Huafeng Dong, Guangdong University of Technology, China. Email: [e-mail]. Chapter 10. Interatomic Potentials developed through Machine Learning Lin-Wang Wang, Lawrence Berkeley National Laboratory, Berkeley, USA. Email: [e-mail] Chapter 11. Discovery of new Compounds Arthur Mar, Department of Chemistry, University of Alberta, Canada. E-mail: [e-mail]. Chapter 12. Defect Dynamics Probed by Using Machine Learning and Experiment. Anja Aarva, Aalto University, 02150 Espoo, Finland. E-mail: [e-mail]. Chapter 13. Machine-Learning Analysis to Predict electronic properties Xi Zhu, The Chinese University of Hong Kong, E-mail: [e-mail]. Chapter 14. Machine-Learning Analysis to Predict spin properties Dmitry V. Krasnikov, Skolkovo Institute of Science and Technology, Russian Federation. E-mail: [e-mail]. Chapter 15. Determination of Material and Structural Parameters using Two-way Neural Network Xu Han, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China. E-mail: [e-mail]

by "Nielsen BookData"

Details

• NCID: BC07889379
• ISBN:
  • 9783030683092
• Country Code: sz
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Cham
• Pages/Volumes: vii, 228 p.
• Size: 25 cm
• Parent Bibliography ID:
  • BA00476831