Artificial Intelligence-Aided Materials Design

<p>This book describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials including hard and soft magnetic alloys nickel-base superalloys titanium-base alloys and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the MATLAB® and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. </p><ul> <p> </p> <li>Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats </li> <p> </p> <li>Helps readers to develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write code </li> <p> </p> <li>Covers downloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devices </li> <p> </p> <li>Discusses the CALPHAD approach and ways to use data generated from it</li> <p> </p> <li>Features a chapter on metallurgical/materials concepts to help readers understand the case studies and thus proper implementation of AI/ML algorithms under the framework of data-driven materials science </li> <p> </p> <li>Uses case studies to examine the importance of using unsupervised machine learning algorithms in determining patterns in datasets</li> </ul><p>This book is written for materials scientists and metallurgists interested in the application of AI ML and data science in the development of new materials.</p>