Overview
- One of the first books on materials discovery strategy
- Emphasizes the paradigm of codesign
- Brings together diverse expertise to improve the model for materials discovery
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Materials Science (SSMATERIALS, volume 225)
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Table of contents (14 chapters)
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Front Matter
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Data Analytics and Optimal Learning
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Front Matter
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Materials Prediction with Data, Simulations and High-throughput Calculations
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Front Matter
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Combinatorial Materials Science with High-throughput Measurements and Analysis
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Front Matter
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Back Matter
Keywords
- Accelerated Materials Discovery
- Applying MQSPRs
- Code Sign
- Complex Formulations and Molecules
- Data-driven Discovery of Materials
- Datamining in Materials Science
- Informatics for Property-processing Linkage
- Materials Genome
- Materials Informatics
- Minimal Peptide Substrates
- Model-based Classification
- Properties of MAX Phase Compounds
About this book
This book deals with an information-driven approach to plan materials discovery and design, iterative learning. The authors present contrasting but complementary approaches, such as those based on high throughput calculations, combinatorial experiments or data driven discovery, together with machine-learning methods. Similarly, statistical methods successfully applied in other fields, such as biosciences, are presented. The content spans from materials science to information science to reflect the cross-disciplinary nature of the field. A perspective is presented that offers a paradigm (codesign loop for materials design) to involve iteratively learning from experiments and calculations to develop materials with optimum properties. Such a loop requires the elements of incorporating domain materials knowledge, a database of descriptors (the genes), a surrogate or statistical model developed to predict a given property with uncertainties, performing adaptive experimental design to guide the next experiment or calculation and aspects of high throughput calculations as well as experiments. The book is about manufacturing with the aim to halving the time to discover and design new materials. Accelerating discovery relies on using large databases, computation, and mathematics in the material sciences in a manner similar to the way used to in the Human Genome Initiative. Novel approaches are therefore called to explore the enormous phase space presented by complex materials and processes. To achieve the desired performance gains, a predictive capability is needed to guide experiments and computations in the most fruitful directions by reducing not successful trials. Despite advances in computation and experimental techniques, generating vast arrays of data; without a clear way of linkage to models, the full value of data driven discovery cannot be realized. Hence, along with experimental, theoretical and computational materials science, we need to add a “fourth leg’’ toour toolkit to make the “Materials Genome'' a reality, the science of Materials Informatics.
Editors and Affiliations
Bibliographic Information
Book Title: Information Science for Materials Discovery and Design
Editors: Turab Lookman, Francis J. Alexander, Krishna Rajan
Series Title: Springer Series in Materials Science
DOI: https://doi.org/10.1007/978-3-319-23871-5
Publisher: Springer Cham
eBook Packages: Chemistry and Materials Science, Chemistry and Material Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-23870-8Published: 28 December 2015
Softcover ISBN: 978-3-319-79541-6Published: 27 March 2019
eBook ISBN: 978-3-319-23871-5Published: 12 December 2015
Series ISSN: 0933-033X
Series E-ISSN: 2196-2812
Edition Number: 1
Number of Pages: XVII, 307
Number of Illustrations: 46 b/w illustrations, 88 illustrations in colour
Topics: Nanotechnology, Materials Engineering, Data Mining and Knowledge Discovery, Complex Systems, Characterization and Evaluation of Materials, Statistical Physics and Dynamical Systems