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Informatics for materials science and engineering : data-driven discovery for accelerated experimentation and application /

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Rajan, Krishna
Format: eBook
Language:English
Published: Oxford : Butterworth-Heinemann, 2013.
Edition:1st ed.
Subjects:
Data mining.
Materials > Data processing.
Materials science.
Exploration de données (Informatique)
Matériaux > Informatique.
Science des matériaux.
Data mining
Materials > Data processing
Materials science
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Machine generated contents note: 1.Materials Informatics: An Introduction / Krishna Rajan
  • 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
  • 2.Data Mining in Materials Science and Engineering / Ya Ju Fan
  • 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
  • 3.Novel Approaches to Statistical Learning in Materials Science / T. Lookman
  • 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
  • Note continued: 8.Materials Science Example: The Search for New Piezoelectrics
  • 9.Conclusion
  • 10.Further Reading
  • Acknowledgments
  • References
  • 4.Cluster Analysis: Finding Groups in Data / Somnath Datta
  • 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
  • 5.Evolutionary Data-Driven Modeling / Nirupam Chakraborti
  • 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
  • 6.Data Dimensionality Reduction in Materials Science / B. Ganapathysubramanian
  • 1.Introduction
  • Note continued: 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
  • 7.Visualization in Materials Research: Rendering Strategies of Large Data Sets / Richard Lesar
  • 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
  • 8.Ontologies and Databases
  • Knowledge Engineering for Materials Informatics / Joseph Glick
  • 1.Introduction
  • 2.Ontologies
  • 3.Databases
  • 4.Conclusions and Further Reading
  • References
  • Websites
  • 9.Experimental Design for Combinatorial Experiments / James N. Cawse
  • 1.Introduction
  • Note continued: 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 / David Cebon
  • 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
  • 11.Thermodynamic Databases and Phase Diagrams / S.K. Saxena
  • 1.Introduction
  • 2.Thermodynamic Databases
  • 3.Examples of Phase Diagrams
  • References
  • 12.Towards Rational Design of Sensing Materials from Combinatorial Experiments / Radislav Potyrailo
  • 1.Introduction
  • Note continued: 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
  • 13.High-Performance Computing for Accelerated Zeolitic Materials Modeling / Pierre Collet
  • 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
  • 14.Evolutionary Algorithms Applied to Electronic-Structure Informatics: Accelerated Materials Design Using Data Discovery vs. Data Searching / Duane D. Johnson
  • 1.Introduction
  • 2.Intuitive Approach to Correlations
  • 3.Genetic Programming for Symbolic Regression
  • Note continued: 4.Constitutive Relations Via Genetic Programming
  • 5.Further Reading
  • Acknowledgments
  • References
  • 15.Informatics for Crystallography: Designing Structure Maps / Krishna Rajan
  • 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 / Curt M. Breneman
  • 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
  • 17.Organic Photovoltaics / Alan Aspuru-Guzik
  • 1.Chemical Space, Energy Sources, and the Clean Energy Project
  • Note continued: 2.The Molecular Library
  • 3.Merit Figures for Organic Photovoltaics
  • 4.Descriptors for Organic Photovoltaics
  • 5.Predictions from Cheminformatics
  • 6.Conclusions
  • Acknowledgments
  • References
  • 18.Microstructure Informatics / Surya R. Kalidindi
  • 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
  • 19.Artworks and Cultural Heritage Materials: Using Multivariate Analysis to Answer Conservation Questions / Carl Villis
  • 1.Rock Art Petroglyphs Examined with Reflectance NIR Spectroscopy and PCA
  • 2.Adhesives Study of Cypriot Pottery Collection with FTIR Spectroscopy and PCA
  • Note continued: 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
  • 20.Data Intensive Imaging and Microscopy: A Multidimensional Data Challenge / Krishna Rajan
  • 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.