Informatics for Materials Science and Engineering : Data-driven Discovery for Accelerated Experimentation and Application.

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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 materi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rajan, Krishna
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Elsevier Science & Technology, 2013.
Temas:
Materials science > Data processing.
Science des matériaux > Informatique.
Materials science > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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 FilterChi-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 Learning6. 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 Results4.1 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 Science1. 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.

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