Provides everything readers need to know for applying the power of informatics to materials science

There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials.

Materials Informatics: Methods, Tools, and Applications is presented in two parts--Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others.

• Bridges the gap between materials science and informatics
• Covers all the known methodologies and applications of materials informatics
• Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials
• Examines the state-of-the-art software and tools being used today

Materials Informatics: Methods, Tools, and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics.

A one-stop source on recent advances in the application of data mining to materials science, this book provides an up-to-date overview of the latest software and tools.
Clearly structured and divided into two parts, the first focuses on current progress in data mining and machine learning for materials science, while the second part addresses developments in software, databases and high-throughput computational activities.
Successful case studies illustrate the power of materials informatics in guiding the experimental discovery of novel materials. A practical approach is maintained throughout, with hints on how to use already existing databases of materials' properties and an accompanying website with interactive applications.
A must-have for material scientists, chemists and engineers interested in these time-saving methods.