Informatics for Materials Science and Engineering
Data-driven Discovery for Accelerated Experimentation and Application
Published on 30. October 2018
Book
Paperback/Softback
542 pages
978-0-12-810121-6 (ISBN)
Description
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.
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.
Reviews / Votes
"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 2013More details
Language
English
Place of publication
Woburn
United States
Publishing group
Elsevier - Health Sciences Division
Target group
Professional and scholarly
Computational materials scientists, combinatorial and high-throughput experimentalists and affiliated applications specialists.
Illustrations
Approx. 110 illustrations
Dimensions
Height: 229 mm
Width: 152 mm
Weight
450 gr
ISBN-13
978-0-12-810121-6 (9780128101216)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Krishna Rajan
Informatics for Materials Science and Engineering
Data-driven Discovery for Accelerated Experimentation and Application
Book
07/2013
Butterworth-Heinemann
€164.50
Shipment within 15-20 days
Person
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.
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.
Content
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 Descriptors, 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
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 Descriptors, 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