Yuan Cheng (editor) - Artificial Intelligence for Materials Science (Springer Series in Materials Science, 312)

The Springer Series in Materials Science covers the complete spectrum of materials research and technology, including fundamental principles, physical properties, materials theory and design. Recognizing the increasing importance of materials science in future device technologies, the book titles in this series reflect the state-of-the-art in understanding and controlling the structure and properties of all important classes of materials.

More information about this series at http://www.springer.com/series/856

This Springer imprint is published by the registered company Springer Nature Switzerland AG

The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Developing algorithmic approaches for the design and discovery of new functional materials can have huge technological and social impact. Usually, such rational design requires a holistic perspective over the full multistage design process. With big data generated by theory and experiments, machine learning approaches have been extensively employed in materials genome initiatives and materials informatics, which can potentially solve some of our challenges on the way to rational materials design. Over the last few decades, materials research has shifted toward more rational design. There are now many examples, such as the accelerated discovery of thermoelectric materials, high-entropy alloys, and thermal functional materials.

Machine learning methods have lowered the cost of exploring new structures of unknown compounds. Furthermore, machine learning methods can be used to predict reasonable expectations, and then the output of the machine learning models can be validated by experimental results. In recent years, new insight has been revealed, and several elaborative tools have been developed for materials science and engineering. Moreover, searchable and interactive databases could promote research regarding emerging materials. Recently, the databases containing a large number of high-quality material properties for new advanced materials discovery have been developed. The development of machine learning will allow us to pursue our aim of understanding and designing of materials in a new way. Moreover, it looks set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic over the coming years.

This book sets the subject into context by first of all describing the chief advancements of these artificial intelligence methods and their applications in materials design. The aim of this book is to provide an introduction both to existing scientific community in this field and for new people who wish to enter it. The book should also be useful for graduate-level students who want to explore this new field of research. With content relevant to both academic and commercial viewpoints, the book will interest researchers and postgraduates as well as consultants and industrial engineers.

The single chapters have been written by internationally recognized experts in computer science and material science and provide in-depth introductions to the directions of their research. Moreover, one chapter outlines the basic information about the AI principles and algorithm, followed by chapters addressing most important and commonly adopted computational and analysis methods in computational material science, and application of these functional materials in various fields, including electronics, optoelectronics, thermoelectric energy conversion, high-entropy alloys, and robotics. We are sure that this book will be a useful reference not only for scientists and engineers exploring material science but also for graduate and postgraduate students specializing in computer, physics, and material science.

We are most grateful to Springer Nature publisher for the invitation to edit this book, and for kind and efficient assistance in editing this book. We are also grateful to all book chapter authors for sharing their expertise in this multi-author monograph. Their strong efforts and enthusiasm for this project were indispensable for bringing it to success.