Front Cover -- Informatics for Materials Science and Engineering -- Copyright Page -- Contents -- Preface: A Reading Guide -- Acknowledgment -- 1. Materials Informatics: An Introduction -- 1. The What and Why of Informatics -- 2. Learning from Systems Biology: An "Omics" Approach to Mater -- 3. Where Do We Get the Information? -- 4. Data Mining: Data-Driven Materials Research -- References -- 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 -- 5.1 Feature Selection Techniques -- Distance Filter -- Chi-Squared Filter -- Stump Filter -- ReliefF -- 5.2 Feature Transformation Techniques -- Principal Component Analysis (PCA) -- Isomap -- Locally Linear Embedding (LLE) -- Laplacian Eigenmaps -- 5.3 Comparison of Dimension Reduction Methods -- 6. Building Predictive and Descriptive Models -- 6.1 Classification and Regression -- 6.2 Clustering -- 7. Further Reading -- Acknowledgments -- References -- 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 -- 4. Cluster Analysis: Finding Groups in Data -- 1. Introduction -- 2. Unsupervised Learning -- 2.1 Principal Components Analysis -- 2.2 Clustering -- 3. Different Clustering Algorithms and their Implementations in R -- 3.1 Agglomerative Hierarchical -- 3.2 K-Means -- 3.3 Divisive Hierarchical -- 3.4 Partitioning Around Medoids (PAM).

3.5 Fuzzy Analysis (FANNY) -- 4. Validations of Clustering Results -- 4.1 Dunn Index -- 4.2 Silhouette Width -- 4.3 Connectivity -- 5. Rank Aggregation of Clustering Results -- 6. Further Reading -- Acknowledgments -- References -- 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 -- 6.1 BioGP Code -- 7. Analyzing the Variable Response in EvoNN and BioGP -- 8. An Application in the Materials Area -- 9. Further Reading -- References -- 6. Data Dimensionality Reduction in Materials Science -- 1. Introduction -- 2. Dimensionality Reduction: Basic Ideas and Taxonomy -- 3. Dimensionality Reduction Methods: Algorithms, Advantages, and Disadvantages -- 3.1 Principal Component Analysis (PCA) -- PCA Algorithm -- 3.2 Isomap -- Isomap Algorithm -- 3.3 Locally Linear Embedding -- LLE Algorithm -- 3.4 Hessian LLE -- hLLE Algorithm -- 4. Dimensionality Estimators -- 5. Software -- 5.1 Core Functionality -- 5.2 User Interface -- 6. Analyzing Two Material Science Data Sets: Apatites and Organic Solar Cells -- 6.1 Apatite Data -- Dimensionality Estimation -- 6.2 Unraveling Process-Morphology Pathways of Organic Solar Cells using SETDiR -- 7. Further Reading -- References -- 7. Visualization in Materials Research: Rendering Strategies of Large Data Sets -- 1. Introduction -- 2. Graphical Tools for Data Visualization: Case Study for Combinatorial Experiments -- 2.1 Heat Maps -- 2.2 Parallel Coordinates -- 2.3 Radial Visualization -- 2.4 Glyph Plots -- 3. Interactive Visualization: Querying Large Imaging Data Sets -- 3.1 Interactive Visualization of 3D Spatio-Spectral SIMS Data -- 3.2 Interactive Visualization of Atom-Probe Tomography Data.

3.3 Visualization of Simulations -- 4. Suggestions for Further Reading -- Acknowledgments -- References -- 8. Ontologies and Databases - Knowledge Engineering for Materials Informatics -- 1. Introduction -- 2. Ontologies -- 2.1 Ontologies, Vocabularies, and Materials Science Informatics -- 2.2 Challenges and Methods -- Complexity -- Systemic Complexity Modeling Methods -- Cognitive Architectures -- Human Expertise -- Relevance Computation -- Models as Agents -- 3. Databases -- 3.1 Roles -- Limitations -- Big Data -- Addressing Impacts of Complexity -- Knowledge Acquisition and Discovery -- Criticality of Relevance Computation in Big Data -- 3.2 Recent Initiatives -- Knowledge Engineering for Nanoinformatics -- Materials Science Proof of Concept Model -- 4. Conclusions and Further Reading -- References -- Websites -- 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 -- 10. Materials Selection for Engineering Design -- 1. Introduction -- 2. Systematic Selection -- 2.1 Translation -- 2.2 Screening -- 2.3 Ranking -- 2.4 Documentation -- 2.5 Why Are All These Steps Necessary? -- 3. Material Indices -- 3.1 Minimizing Mass: A Light, Strong Tie -- 3.2 Minimizing Mass: A Light, Stiff Panel -- 3.3 Minimizing Mass: A Light, Stiff Beam -- 3.4 Minimizing Material Cost: Cheap Ties, Panels, and Beams -- 4. Using Charts to Explore Material Properties -- 4.1 Screening: Constraints on Charts -- 4.2 Ranking: Indices on Charts -- 5. Practical Materials Selection: Tradeoff Methods -- 6. Material Substitution.

6.1 Using Indices for Scaling -- 7. Vectors for Material Development -- 7.1 Holes in Material Property Space -- 7.2 Hybrid Materials: Driving Development to Fill the Holes -- 7.3 Example of Hybrid Synthesis: Sandwich Structures -- 8. Conclusions and Suggested Further Reading -- References -- 11. Thermodynamic Databases and Phase Diagrams -- 1. Introduction -- 2. Thermodynamic Databases -- 2.1 Current Status of the Databases -- 2.2 An Internally Consistent Database -- 2.3 Problems of Databases -- Gibbs Energy -- Solution Models -- Programs and Principles of Assessment -- Metals and Ceramics at High Pressure -- 3. Examples of Phase Diagrams -- 3.1 Silicides -- Mn-Si -- W-Si -- 3.2 Phase Diagrams with In Situ Information -- 3.3 Phase Composition Diagrams -- Carbothermal Reduction of Silica -- The Mg-H2O System -- The NaOH-CH4 System -- 3.4 Phase Diagrams at High Pressure and Temperature -- The Iron Phase Diagram -- 3.5 Geochemical Phase Diagrams -- Solar-Gas Condensation -- Thermodynamic Evaluation of the Surface Rocks on Mars -- Planetary Interiors (Mars) -- 3.6 Some Databases of Interest -- References -- 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 -- 5.1 Radiant Energy Transduction Sensors -- 5.2 Mechanical Energy Transduction Sensors -- 5.3 Electrical Energy Transduction Sensors -- 6. Optimization of Sensing Materials Using Gradient Arrays -- 6.1 Variable Concentration and Composition of Reagents -- 6.2 Variable Operation Temperature -- 7. Summary and Outlook -- 8. Further Reading -- Acknowledgments -- References.

13. High-Performance Computing for Accelerated Zeolitic Materials Modeling -- 1. Introduction -- 2. GPGPU-Based Genetic Algorithms -- 3. Standard Optimization Benchmarks -- 3.1 Weierstrass-Mandelbrot Functions -- 3.2 Rosenbrock Function -- 4. Fast Generation of Four-Connected 3D Nets for Modeling Zeolite Structures -- 5. Real Zeolite Problem -- 5.1 Benchmark Tests -- 5.2 The MFI Framework -- 6. Further Reading -- References -- 14. Evolutionary Algorithms Applied to Electronic-Structure Informatics: Accelerated Materials Design Using Data Discovery v... -- 1. Introduction -- 2. Intuitive Approach to Correlations -- 2.1 Universal Correlations for Nanoparticle Core-Shell Behavior (Alloying) -- Origins from Electronic Structure -- What Was Our Purpose? -- 2.2 Universal Correlations for Molecular Adsorption on TM Surfaces -- Origins from Electronic Structure -- What Was Our Purpose? -- 3. Genetic Programming for Symbolic Regression -- 3.1 What is a GP? -- Fitness -- Population -- 4. Constitutive Relations Via Genetic Programming -- 5. Further Reading -- Acknowledgments -- References -- 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 -- 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 -- 2.1. Constitutional Descriptors and Group Contributions -- 2.2. 2-D Descriptors -- 2.3. 3-D Descriptors -- 2.4. AIM-Derived Descriptors -- 2.5. Vibrational Spectral Descriptors for Materials Applications -- 3. Mathematical Methods for QSPR/QSAR/MQSPR.

3.1 Methods and Machine Learning Workflow.