Informatics for Materials Science and Engineering

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.

• 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

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

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