
Informatics for Materials Science and Engineering
Data-driven Discovery for Accelerated Experimentation and Application
Description
Key Features
- Identifies and analyzes interdisciplinary strategies (including combinatorial and high throughput approaches) that accelerate materials development cycle times and reduces associated costs
- Mathematical and computational analysis aids formulation of new structure-property correlations among large, heterogeneous, and distributed data sets
- Practical examples, computational tools, and software analysis benefits rapid identification of critical data and analysis of theoretical needs for future problems
Readership
Table of Contents
Preface: A Reading Guide
Acknowledgment
Chapter 1. Materials Informatics: An Introduction
1 The What and Why of Informatics
2 Learning from Systems Biology: An “Omics” Approach to Materials Design
3 Where Do We Get the Information?
4 Data Mining: Data-Driven Materials Research
References
Chapter 2. Data Mining in Materials Science and Engineering
1 Introduction
2 Analysis Needs of Science Applications
3 The Scientific Data-Mining Process
4 Image Analysis
5 Dimension Reduction
6 Building Predictive and Descriptive Models
7 Further Reading
Acknowledgments
References
Chapter 3. Novel Approaches to Statistical Learning in Materials Science
1 Introduction
2 The Supervised Binary Classification Learning Problem
3 Incorporating Side Information
4 Conformal Prediction
5 Optimal Learning
6 Optimal Uncertainty Quantification
7 Clustering Including Statistical Physics Approaches
8 Materials Science Example: The Search for New Piezoelectrics
9 Conclusion
10 Further Reading
Acknowledgments
References
Chapter 4. Cluster Analysis: Finding Groups in Data
1 Introduction
2 Unsupervised Learning
3 Different Clustering Algorithms and their Implementations in R
4 Validations of Clustering Results
5 Rank Aggregation of Clustering Results
6 Further Reading
Acknowledgments
References
Chapter 5. Evolutionary Data-Driven Modeling
1 Preamble
2 The Concept of Pareto Tradeoff
3 Evolutionary Neural Net and Pareto Tradeoff
4 Selecting the Appropriate Model in EvoNN
5 Conventional Genetic Programming
6 Bi-Objective Genetic Programming
7 Analyzing the Variable Response in EvoNN and BioGP
8 An Application in the Materials Area
9 Further Reading
References
Chapter 6. Data Dimensionality Reduction in Materials Science
1 Introduction
2 Dimensionality Reduction: Basic Ideas and Taxonomy
3 Dimensionality Reduction Methods: Algorithms, Advantages, and Disadvantages
4 Dimensionality Estimators
5 Software
6 Analyzing Two Material Science Data Sets: Apatites and Organic Solar Cells
7 Further Reading
References
Chapter 7. Visualization in Materials Research: Rendering Strategies of Large Data Sets
1 Introduction
2 Graphical Tools for Data Visualization: Case Study for Combinatorial Experiments
3 Interactive Visualization: Querying Large Imaging Data Sets
4 Suggestions for Further Reading
Acknowledgments
References
Chapter 8. Ontologies and Databases – Knowledge Engineering for Materials Informatics
1 Introduction
2 Ontologies
3 Databases
4 Conclusions and Further Reading
References
Websites
Chapter 9. Experimental Design for Combinatorial Experiments
1 Introduction
2 Standard Design of Experiments (DOE) Methods
3 Mixture (Formulation) Designs
4 Compound Designs
5 Restricted Randomization, Split-Plot, and Related Designs
6 Evolutionary Designs
7 Designs for Determination of Kinetic Parameters
8 Other Methods
9 Gradient Spread Designs
10 Looking Forward
References
Chapter 10. Materials Selection for Engineering Design
1 Introduction
2 Systematic Selection
3 Material Indices
4 Using Charts to Explore Material Properties
5 Practical Materials Selection: Tradeoff Methods
6 Material Substitution
7 Vectors for Material Development
8 Conclusions and Suggested Further Reading
References
Chapter 11. Thermodynamic Databases and Phase Diagrams
1 Introduction
2 Thermodynamic Databases
3 Examples of Phase Diagrams
References
Chapter 12. Towards Rational Design of Sensing Materials from Combinatorial Experiments
1 Introduction
2 General Principles of Combinatorial Materials Screening
3 Opportunities for Sensing Materials
4 Designs of Combinatorial Libraries of Sensing Materials
5 Optimization of Sensing Materials Using Discrete Arrays
6 Optimization of Sensing Materials Using Gradient Arrays
7 Summary and Outlook
8 Further Reading
Acknowledgments
References
Chapter 13. High-Performance Computing for Accelerated Zeolitic Materials Modeling
1 Introduction
2 GPGPU-Based Genetic Algorithms
3 Standard Optimization Benchmarks
4 Fast Generation of Four-Connected 3D Nets for Modeling Zeolite Structures
5 Real Zeolite Problem
6 Further Reading
References
Chapter 14. Evolutionary Algorithms Applied to Electronic-Structure Informatics: Accelerated Materials Design Using Data Discovery vs. Data Searching
1 Introduction
2 Intuitive Approach to Correlations
3 Genetic Programming for Symbolic Regression
4 Constitutive Relations Via Genetic Programming
5 Further Reading
Acknowledgments
References
Chapter 15. Informatics for Crystallography: Designing Structure Maps
1 Introduction
2 Structure Map Design for Complex Inorganic Solids Via Principal Component Analysis
3 Structure Map Design for Intermetallics Via Recursive Partioning
4 Further Reading
References
Chapter 16. From Drug Discovery QSAR to Predictive Materials QSPR: The Evolution of Descriptors, Methods, and Models
1 Historical Perspective
2 The Science of Mqspr: Choice and Design of Material Property Descriptors
3 Mathematical Methods for QSPR/QSAR/MQSPR
4 Integration of Physical and MQSPR Models for Nanocomposite Materials Modeling
5 The Future of Materials Informatics Applications
References
Chapter 17. Organic Photovoltaics
1 Chemical Space, Energy Sources, and the Clean Energy Project
2 The Molecular Library
3 Merit Figures for Organic Photovoltaics
4 Descriptors for Organic Photovoltaics
5 Predictions from Cheminformatics
6 Conclusions
Acknowledgments
References
Chapter 18. Microstructure Informatics
1 Introduction
2 Microstructure Quantification Using Higher-Order Spatial Correlations
3 Objective Reduced-Order Representation of Microstructure
4 Data Science-Enabled Formulation of Structure–Property–Processing (SPP) Linkages
5 Computationally Efficient Scale-Bridging for Multiscale Materials Modeling
6 Further Reading
Acknowledgments
References
Chapter 19. Artworks and Cultural Heritage Materials: Using Multivariate Analysis to Answer Conservation Questions
1 Rock Art Petroglyphs Examined with Reflectance NIR Spectroscopy and PCA
2 Adhesives Study of Cypriot Pottery Collection with FTIR Spectroscopy and PCA
3 Egyptian Sarcophagus Examined with ToF-SIMS, XANES, and PCA
4 Attribution Studies of an Italian Renaissance Painting: ESEM Imaging
5 Ochre Pigments Imaged Using Synchrotron XRF
6 General Summary and Conclusions
References
Chapter 20. Data Intensive Imaging and Microscopy: A Multidimensional Data Challenge
1 Introduction
2 Chemical Imaging in Materials Science: Linking Signal and Spatial Domains
3 Contrast Mining in Spectroscopy: Tracking Processing–Property Relationships
4 Further Reading
References
Index
Product details
- No. of pages: 542
- Language: English
- Copyright: © Butterworth-Heinemann 2013
- Published: July 10, 2013
- Imprint: Butterworth-Heinemann
- eBook ISBN: 9780123946140
- Hardcover ISBN: 9780123943996
About the Editor
Krishna Rajan
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