Informatics for Materials Science and Engineering book cover

Informatics for Materials Science and Engineering

Data-driven Discovery for Accelerated Experimentation and Application

Materials informatics: a ‘hot topic’ area in materials science, aims to combine traditionally bio-led informatics with computational methodologies, supporting more efficient research by identifying strategies for time- and cost-effective analysis.

The discovery and maturation of new materials has been outpaced by the thicket of data created by new combinatorial and high throughput analytical techniques. The elaboration of this "quantitative avalanche"-and the resulting complex, multi-factor analyses required to understand it-means that interest, investment, and research are revisiting informatics approaches as a solution.

This work, from Krishna Rajan, the leading expert of the informatics approach to materials, seeks to break down the barriers between data management, quality standards, data mining, exchange, and storage and analysis, as a means of accelerating scientific research in materials science.

This solutions-based reference synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field.

Audience
Computational materials scientists, combinatorial and high-throughput experimentalists and affiliated applications specialists.

Hardbound, 542 Pages

Published: July 2013

Imprint: Butterworth Heinemann

ISBN: 978-0-12-394399-6

Reviews

  • "The first half of the volume sets out foundational aspects of data science, and the second half surveys applications in materials science using a case-study approach. The topics include novel approaches to statistical learning in materials science, data dimensionality reduction in materials science,…. high-performance computing for accelerated zeolitic materials modeling, and using multivariate analysis to answer questions concerning the conservation of artworks and cultural heritage materials."--Reference & Research Book News, December 2013


Contents

  • Preface: A Reading Guide xiii
    Acknowledgment xv
    1. Materials Informatics: An Introduction 1
    2. Data Mining in Materials Science and Engineering 17
    3. Novel Approaches to Statistical Learning in Materials Science 37
    4. Cluster Analysis: Finding Groups in Data 53
    5. Evolutionary Data-Driven Modeling 71
    6. Data Dimensionality Reduction in Materials Science 97
    7. Visualization in Materials Research: Rendering Strategies
    of Large Data Sets 121
    8. Ontologies and Databases < Knowledge Engineering
    for Materials Informatics 147
    9. Experimental Design for Combinatorial Experiments 189
    10. Materials Selection for Engineering Design 219
    11. Thermodynamic Databases and Phase Diagrams 245
    12. Towards Rational Design of Sensing Materials
    from Combinatorial Experiments 271
    13. High-Performance Computing for Accelerated Zeolitic
    Materials Modeling 315
    14. Evolutionary Algorithms Applied to Electronic-Structure
    Informatics: Accelerated Materials Design Using Data
    Discovery vs. Data Searching 349
    15. Informatics for Crystallography: Designing Structure Maps 365
    16. From Drug Discovery QSAR to Predictive Materials QSPR:
    The Evolution of Descriptors, Methods, and Models 385
    17. Organic Photovoltaics 423
    18. Microstructure Informatics 443
    19. Artworks and Cultural Heritage Materials: Using Multivariate
    Analysis to Answer Conservation Questions 467
    20. Data Intensive Imaging and Microscopy: A Multidimensional
    Data Challenge 495
    References 510
    Index 513

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