Materials Informatics: Methods, Tools, and Applications provides an comprehensive guide to the latest advances in data mining and machine learning techniques applied to materials science. Edited by Olexandr Isayev and published by Wiley-VCH in 2019, this book bridges the gap between materials science and informatics, offering a detailed exploration of both methodological and practical aspects. The first part focuses on developments in software, databases, and high-throughput computational activities, including open quantum materials databases, ICSD database, and open crystallography databases. The second part delves into data mining and machine learning for materials science, covering topics such as genetic algorithms and crystal structure prediction, MQSPR modeling, and property prediction. Case studies illustrate the power of materials informatics in guiding experimental discovery, making it an essential resource for materials scientists, chemists, and engineers. With its comprehensive coverage and practical insights, this book is a must-have for anyone interested in the methods of materials informatics.