- 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
Redaktion: Krishna Rajan
Krishna Rajan is the SUNY Distinguished Professor and Erich Bloch Chair of the Department of Materials Design and Innovation (MDI) at the University at Buffalo; with a joint appointment as Chief Scientist in the Energy Processes and Materials Division at Pacific Northwest National Laboratory (PNNL). He has pioneered the field of Materials Informatics and data driven discovery in materials science and engineering and its impact on characterization, processing, and modeling of materials. He has received numerous recognitions including the Alexander von Humboldt Award from Germany, the CSIRO- Australia Distinguished Visiting Scientist Award, the CNRS Visiting Professorship from France and the Presidential Lecture Award from the National Institute of Materials Science, Japan.
Dr Rajan received his undergraduate degree in Metallurgy and Materials Science from the University of Toronto followed by a doctorate in Materials Science from MIT with a minor in Science and Technology policy. He subsequently held post-doctoral appointments at MIT and Cambridge University. He was a staff scientist at the National Research Council of Canada, followed by faculty positions at Rensselaer Polytechnic Institute and Iowa State University before coming to the University at Buffalo as the founding chair of the MDI department. It is the first department that has its research and curriculum built around an informatics perspective of materials science and engineering.
Encompasses the mathematical and computational foundations of materials informatics, from experimental to real-world applications
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.
Preface: A Reading Guide xiii
Acknowledgment xv
1. Materials Informatics: An Introduction 1
2. Data Mining in Materials Science and Engineering 17
3. Novel Approaches to Statistical Learning in Materials Science 37
4. Cluster Analysis: Finding Groups in Data 53
5. Evolutionary Data-Driven Modeling 71
6. Data Dimensionality Reduction in Materials Science 97
7. Visualization in Materials Research: Rendering Strategies
of Large Data Sets 121
8. Ontologies and Databases < Knowledge Engineering
for Materials Informatics 147
9. Experimental Design for Combinatorial Experiments 189
10. Materials Selection for Engineering Design 219
11. Thermodynamic Databases and Phase Diagrams 245
12. Towards Rational Design of Sensing Materials
from Combinatorial Experiments 271
13. High-Performance Computing for Accelerated Zeolitic
Materials Modeling 315
14. Evolutionary Algorithms Applied to Electronic-Structure
Informatics: Accelerated Materials Design Using Data
Discovery vs. Data Searching 349
15. Informatics for Crystallography: Designing Structure Maps 365
16. From Drug Discovery QSAR to Predictive Materials QSPR:
The Evolution of Deillegalscriptors, Methods, and Models 385
17. Organic Photovoltaics 423
18. Microstructure Informatics 443
19. Artworks and Cultural Heritage Materials: Using Multivariate
Analysis to Answer Conservation Questions 467
20. Data Intensive Imaging and Microscopy: A Multidimensional
Data Challenge 495
References 510
Index 513
"The first half of the volume sets out foundational aspects of data science, and the second half surveys applications in materials science using a case-study approach. The topics include novel approaches to statistical learning in materials science, data dimensionality reduction in materials science,.... high-performance computing for accelerated zeolitic materials modeling, and using multivariate analysis to answer questions concerning the conservation of artworks and cultural heritage materials." --Reference & Research Book News, December 2013
Synthesizes foundational physical, statistical, and mathematical content with emerging experimental and real-world applications, for interdisciplinary researchers and those new to the field. This title identifies and analyzes strategies that accelerate materials development cycle times and reduces associated costs.
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.
Preface: A Reading Guide xiii Acknowledgment xv 1. Materials Informatics: An Introduction 1 2. Data Mining in Materials Science and Engineering 17 3. Novel Approaches to Statistical Learning in Materials Science 37 4. Cluster Analysis: Finding Groups in Data 53 5. Evolutionary Data-Driven Modeling 71 6. Data Dimensionality Reduction in Materials Science 97 7. Visualization in Materials Research: Rendering Strategies of Large Data Sets 121 8. Ontologies and Databases < Knowledge Engineering for Materials Informatics 147 9. Experimental Design for Combinatorial Experiments 189 10. Materials Selection for Engineering Design 219 11. Thermodynamic Databases and Phase Diagrams 245 12. Towards Rational Design of Sensing Materials from Combinatorial Experiments 271 13. High-Performance Computing for Accelerated Zeolitic Materials Modeling 315 14. Evolutionary Algorithms Applied to Electronic-Structure Informatics: Accelerated Materials Design Using Data Discovery vs. Data Searching 349 15. Informatics for Crystallography: Designing Structure Maps 365 16. From Drug Discovery QSAR to Predictive Materials QSPR: The Evolution of Deillegalscriptors, Methods, and Models 385 17. Organic Photovoltaics 423 18. Microstructure Informatics 443 19. Artworks and Cultural Heritage Materials: Using Multivariate Analysis to Answer Conservation Questions 467 20. Data Intensive Imaging and Microscopy: A Multidimensional Data Challenge 495 References 510 Index 513
"The first half of the volume sets out foundational aspects of data science, and the second half surveys applications in materials science using a case-study approach. The topics include novel approaches to statistical learning in materials science, data dimensionality reduction in materials science,.. high-performance computing for accelerated zeolitic materials modeling, and using multivariate analysis to answer questions concerning the conservation of artworks and cultural heritage materials." --Reference & Research Book News, December 2013
Klappentext
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 have 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.
The work, from Krishna Rajan, the leading exponent of the informatics approach to materials, seeks to break down the barriers among data management, quality standards, data mining, exchange, 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.
