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Materials Informatics: Methods, Tools, and Applications

Hardcover

Pre-order

€141.30

*VAT

Materials Informatics: Methods, Tools, and Applications

Hardcover
Pre-order
€141.30

Description

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.
METHODOLOGICAL ASPECTS OF MATERIALS INFORMATICS
Big Data in Chemistry
Genetic Algorithms, Crystal Structure Prediction
Machine Learning in Materials Science
MQSPR Modeling in Materials Informatics
Machine Learning Predictions of Molecular Properties
Statistical Modeling for Material Databases
Machine Learning Models in Chemical Space
Big Data in Materials Informatics
Topological Analysis of Crystal Structures

SOFTWARE AND TOOLS FOR MATERIALS INFORMATICS
AFLOWLIB
Harvard Clean Energy
Python Software for MI
High-Throughput Computational Screening of Materials
Open Quantum Materials Database
Computational Materials Repository
ICSD Database
Open Crystallography Database