Artificial Intelligence for Materials Science
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.
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Persons
Dr. Tian Wang. Dr. Wang received 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.
Content
Chapter 1. M aterials Genome Initiatives: Past, Present, and Prospect
Gang Zhang, Institute of High Performance Computing, A*STAR, 138632 Singapore. zhangg@ihpc.a-star.edu.sg
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: wangtian@buaa.edu.cn2. Institute Charles Delaunay-LM2S FRE CNRS 2019, University of Technology of Troyes, Troyes 10030, France. Email: hichem.snouss@utt.fr
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. huajian.gao@ntu.edu.sg
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: mbuehler@MIT.EDU
Chapter 5. Rapid Photovoltaic Device Characterization through AI technology
Tonio Buonassisi, MIT. Email: BUONASSISI@MIT.EDU
Chapter 6. Machine learning for thermal contact design
Junichiro Shiomi, The University of Tokyo, Japan. shiomi@photon.t.u-tokyo.ac.jp
Chapter 7. Discovery of new thermoelectric material through high-throughput calculation
Wenqing Zhang, Southern University of Science and Technology, China. zhangwq@sustc.edu.cn
Chapter 8. Machine learning for high heat conductive material
Eric S. Toberer, Colorado School of Mines, USA. E-mail: etoberer@mines.edu
Chapter 9. Machine learning assisted discovery of new 2D Materials
Huafeng Dong, Guangdong University of Technology, China. Email: hfdong@gdut.edu.cn.
Chapter 10. Interatomic Potentials developed through Machine Learning
Lin-Wang Wang, Lawrence Berkeley National Laboratory, Berkeley, USA. Email: lwwang@lbl.gov
Chapter 11. Discovery of new Compounds
Arthur Mar, Department of Chemistry, University of Alberta, Canada. E-mail: amar@ualberta.ca.
Chapter 12. Defect Dynamics Probed by Using Machine Learning and Experiment.
Anja Aarva, Aalto University, 02150 Espoo, Finland. E-mail: anja.aarva@aalto.fi.
Chapter 13. Machine-Learning Analysis to Predict electronic properties
Xi Zhu, The Chinese University of Hong Kong, E-mail: zhuxi@cuhk.edu.cn.
Chapter 14. Machine-Learning Analysis to Predict spin properties
Dmitry V. Krasnikov, Skolkovo Institute of Science and Technology, Russian Federation. E-mail: d.krasnikov@skoltech.ru.
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: xhan@hebut.edu.cn