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Materials Informatics

Materials Informatics

Olexandr Isayev, Alexander Tropsha, Stefano Curtarolo

ISBN: 978-3-527-34121-4

Sep 2020

450 pages

Select type: Hardcover

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Description

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.
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