This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.

Scaffold bone replacements are a safe and effective way to cure bone abnormalities, and porous scaffolds can be manufactured using additive manufacturing technology. When scaffolds are implanted in a damaged location, they quickly connect to the host tissue and integrate, stimulating bone production and development. The qualities of porous titanium must be matched to the properties of human bones (i.e., age, sex, and hormones). Using subtractive manufacturing, it is extremely difficult to create the complicated porous structure necessary for the desired characteristic. The Handbook of Research on Advanced Functional Materials for Orthopedic Applications highlights current research pertinent to the orthopedic applications of additive-produced scaffolds in order to consider the latest breakthroughs in the synthesis and multifunctional applications of scaffolds. Covering key topics such as tissue, additive manufacturing, and biomaterial, this major reference work is ideal for industry professionals, engineers, researchers, academicians, practitioners, scholars, instructors, and students.

This book highlights the significance and usefulness of nanomaterials for the development of sensing devices and their real-life applications. The book also addresses various means of synthesizing functional materials, e.g., hydrothermal deposition process, electrospinning, Ostwald ripening, sputtering heterogeneous deposition, liquid-phase preparation, the vapor deposition approach, and aerosol flame synthesis. It presents an informative overview of the role of functional materials in the development of advanced sensor devices at the nanoscale and discusses the applications of functional materials in different forms prepared by diverse techniques in the field of optoelectronics and biomedical devices. Major features, such as type of advanced functional, fabrication methods, applications, tasks, benefits and restrictions, and saleable features, are presented in this book. Advanced functional materials for sensing have much wider applications and have an enormous impact on our environment.

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.

The self-learning ability of machine learning algorithms makes the investigations more accurate and accommodates all the complex requirements. Development in neural codes can accommodate the data in all the forms such as numerical values as well as images. The techniques also review the sustainability, life-span, the energy consumption in production polymer, etc. This book addresses the design, characterization, and development of prediction analysis of sustainable polymer composites using machine learning algorithms.

This book, which is part of a two-volume handbook set, gives a comprehensive description of recent developments in materials science and manufacturing technology, aiming primarily at its applications in biomedical science, advanced engineering materials, conventional/non-conventional manufacturing techniques, sustainable engineering design, and related domains. Manufacturing Engineering and Materials Science: Tools and Applications provides state-of-the-art research conducted in the fields of technological advancements in surface engineering, tribology, additive manufacturing, precision manufacturing, electromechanical systems, and computer-assisted design and manufacturing. The book captures emerging areas of materials science and advanced manufacturing engineering and presents the most recent trends in research for emerging researchers, field engineers, and academic professionals.