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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 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
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Illustrationen
Approx. 110 illustrations
ISBN-13
978-0-12-394614-0 (9780123946140)
Schweitzer Klassifikation
Preface: A Reading Guide xiiiAcknowledgment xv1. Materials Informatics: An Introduction 12. Data Mining in Materials Science and Engineering 173. Novel Approaches to Statistical Learning in Materials Science 374. Cluster Analysis: Finding Groups in Data 535. Evolutionary Data-Driven Modeling 716. Data Dimensionality Reduction in Materials Science 977. Visualization in Materials Research: Rendering Strategiesof Large Data Sets 1218. Ontologies and Databases for Materials Informatics 1479. Experimental Design for Combinatorial Experiments 18910. Materials Selection for Engineering Design 21911. Thermodynamic Databases and Phase Diagrams 24512. Towards Rational Design of Sensing Materialsfrom Combinatorial Experiments 27113. High-Performance Computing for Accelerated ZeoliticMaterials Modeling 31514. Evolutionary Algorithms Applied to Electronic-StructureInformatics: Accelerated Materials Design Using DataDiscovery vs. Data Searching 34915. Informatics for Crystallography: Designing Structure Maps 36516. From Drug Discovery QSAR to Predictive Materials QSPR:The Evolution of Descriptors, Methods, and Models 38517. Organic Photovoltaics 42318. Microstructure Informatics 44319. Artworks and Cultural Heritage Materials: Using MultivariateAnalysis to Answer Conservation Questions 46720. Data Intensive Imaging and Microscopy: A MultidimensionalData Challenge 495References 510Index 513