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This book introduces how the rapid advancement of artificial intelligence AI is transforming the field of materials informatics, viz., AI+ materials. Around the world, self-driving materials laboratories and robotic systems the hard foundation of this field are emerging with remarkable speed. At the same time, a diverse array of AI-driven algorithms and computational tools the soft foundation are accelerating materials discovery and design. In particular, the development of large materials models has been significantly propelled by large language models. We are now entering the AI era, where AI for science (AI4S), AI for engineering (AI4E), and AI for materials (AI4M) are extremely important aspects. In this context, it is essential to equip materials scientists, engineers, and students with a solid understanding of fundamental and advanced AI methods and algorithms and their applications in materials science and engineering.
List of contents
Chapter 1 Bayesian global optimization.- Chapter 2 Swarm based optimization algorithms.- Chapter 3 Transfer learning.
About the author
Tongyi Zhang, is Expert in the interdisciplinary area of materials science and engineering and solid mechanics. He received his Ph.D. in 1985 in materials physics from the University of Science and Technology Beijing, China. He was Research Fellow of the Alexander von Humboldt Foundation, 1986-1988, Germany; Postdoctoral Fellow, 1988-1990, University of Rochester; and Associate Research Scientist, 1990-1993, Yale University. From 1993 to 2015, he worked at the Hong Kong University of Science and Technology, as Lecturer, Associate Professor, Professor, Chair Professor, and Fang Professor of Engineering.
He is the founding dean of Materials Genome Institute, Shanghai University, the founding director of the Materials Genome Engineering division in the Chinese Materials Research Society (CMRS), and the founding director of the Guangzhou Municipal Key Laboratory of Materials Informatics. Currently he is doing his best to promote Materials Genome Engineering, Materials/Mechanics Informatics and AI+materials, especially, materials AI labs, materials AI computations, and materials large multimodal model.
He is the Vice President of the International Congress on Fracture, the Honorary President of the Chinese Society for Corrosion and Protection, and was the Vice President of the Far East and Oceanic Fracture Society. He is the founding Editor-in-Chief of Journal of Materials Informatics and was the Editor-in-Chief of Science China Technological Sciences 2018-2022. He became Fellow of International Congress on Fracture in 2013, Fellow of the Hong Kong Academy of Engineering Sciences in 2012, Member of Chinese Academy of Sciences in 2011, Senior Research Fellow of Croucher Foundation, Hong Kong, in 2003, Fellow of ASM International, USA, in 2001. He received the awards including the 2024 outstanding contribution award to materials genome engineering, China, the 2018 Prize for Scientific and Technological Progress from the HLHL Foundation, the Second Prizes of 2007 and 1987 State Natural Science Award, China, and the 1988 National Award for Young Scientists, China.
Summary
This book introduces how the rapid advancement of artificial intelligence（AI） is transforming the field of materials informatics, viz., AI+ materials. Around the world, self-driving materials laboratories and robotic systems—the “hard foundation” of this field—are emerging with remarkable speed. At the same time, a diverse array of AI-driven algorithms and computational tools—the “soft foundation”—are accelerating materials discovery and design. In particular, the development of large materials models has been significantly propelled by large language models. We are now entering the AI era, where AI for science (AI4S), AI for engineering (AI4E), and AI for materials (AI4M) are extremely important aspects. In this context, it is essential to equip materials scientists, engineers, and students with a solid understanding of fundamental and advanced AI methods and algorithms and their applications in materials science and engineering.
