Coverart for item
The Resource Materials Discovery and Design : By Means of Data Science and Optimal Learning, Turab Lookman, Stephan Eidenbenz, Frank Alexander, Cris Barnes, editors

Materials Discovery and Design : By Means of Data Science and Optimal Learning, Turab Lookman, Stephan Eidenbenz, Frank Alexander, Cris Barnes, editors

Label
Materials Discovery and Design : By Means of Data Science and Optimal Learning
Title
Materials Discovery and Design
Title remainder
By Means of Data Science and Optimal Learning
Statement of responsibility
Turab Lookman, Stephan Eidenbenz, Frank Alexander, Cris Barnes, editors
Contributor
Editor
Subject
Language
eng
Summary
This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science and cyber related applications. The challenging open questions as well as future directions in the application of data science to materials problems are sketched. Computational and experimental facilities today generate vast amounts of data at an unprecedented rate. The book gives guidance to discover new knowledge that enables materials innovation to address grand challenges in energy, environment and security, the clearer link needed between the data from these facilities and the theory and underlying science. The role of inference and optimization methods in distilling the data and constraining predictions using insights and results from theory is key to achieving the desired goals of real time analysis and feedback. Thus, the importance of this book lies in emphasizing that the full value of knowledge driven discovery using data can only be realized by integrating statistical and information sciences with materials science, which is increasingly dependent on high throughput and large scale computational and experimental data gathering efforts. This is especially the case as we enter a new era of big data in materials science with the planning of future experimental facilities such as the Linac Coherent Light Source at Stanford (LCLS-II), the European X-ray Free Electron Laser (EXFEL) and MaRIE (Matter Radiation in Extremes), the signature concept facility from Los Alamos National Laboratory. These facilities are expected to generate hundreds of terabytes to several petabytes of in situ spatially and temporally resolved data per sample. The questions that then arise include how we can learn from the data to accelerate the processing and analysis of reconstructed microstructure, rapidly map spatially resolved properties from high throughput data, devise diagnostics for pattern detection, and guide experiments towards desired targeted properties. The authors are an interdisciplinary group of leading experts who bring the excitement of the nascent and rapidly emerging field of materials informatics to the reader
Member of
Cataloging source
N$T
Dewey number
620.11
Index
index present
LC call number
TA403.9
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Lookman, Turab
  • Eidenbenz, Stephan
  • Alexander, Frank
  • Barnes, Cris
Series statement
Springer series in materials science
Series volume
volume 280
http://library.link/vocab/subjectName
  • Materials science
  • Data mining
Label
Materials Discovery and Design : By Means of Data Science and Optimal Learning, Turab Lookman, Stephan Eidenbenz, Frank Alexander, Cris Barnes, editors
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Contents; Contributors; 1 Dimensions, Bits, and Wows in Accelerating Materials Discovery; 1.1 Introduction; 1.2 Creativity and Discovery; 1.3 Discovering Dimensions; 1.4 Infotaxis; 1.5 Pursuit of Bayesian Surprise; 1.6 Conclusion; References; 2 Is Automated Materials Design and Discovery Possible?; 2.1 Model Determination in Materials Science; 2.1.1 The Status Quo; 2.1.2 The Goal; 2.2 Identification of the Research and Issues; 2.2.1 Reducing the Degrees of Freedom in Model Determination; 2.2.2 OUQ and mystic; 2.3 Introduction to Uncertainty Quantification
  • 2.3.1 The UQ Problem2.4 Generalizations and Comparisons; 2.4.1 Prediction, Extrapolation, Verification and Validation; 2.4.2 Comparisons with Other UQ Methods; 2.5 Optimal Uncertainty Quantification; 2.5.1 First Description; 2.6 The Optimal UQ Problem; 2.6.1 From Theory to Computation; 2.7 Optimal Design; 2.7.1 The Optimal UQ Loop; 2.8 Model-Form Uncertainty; 2.8.1 Optimal UQ and Model Error; 2.8.2 Game-Theoretic Formulation and Model Error; 2.9 Design and Decision-Making Under Uncertainty; 2.9.1 Optimal UQ for Vulnerability Identification; 2.9.2 Data Collection for Design Optimization
  • 2.10 A Software Framework for Optimization and UQ in Reduced Search Space2.10.1 Optimization and UQ; 2.10.2 A Highly-Configurable Optimization Framework; 2.10.3 Reduction of Search Space; 2.10.4 New Massively-Parallel Optimization Algorithms; 2.10.5 Probability and Uncertainty Tooklit; 2.11 Scalability; 2.11.1 Scalability Through Asynchronous Parallel Computing; References; 3 Importance of Feature Selection in Machine Learning and Adaptive Design for Materials; 3.1 Introduction; 3.2 Computational Details; 3.2.1 Density Functional Theory; 3.2.2 Machine Learning; 3.2.3 Design; 3.3 Results
  • 3.4 Discussion3.5 Summary; References; 4 Bayesian Approaches to Uncertainty Quantification and Structure Refinement from X-Ray Diffraction; 4.1 Introduction; 4.2 Classical Methods of Structure Refinement; 4.2.1 Classical Single Peak Fitting; 4.2.2 The Rietveld Method; 4.2.3 Frequentist Inference and Its Limitations; 4.3 Bayesian Inference; 4.3.1 Sampling Algorithms; 4.4 Application of Bayesian Inference to Single Peak Fitting: A Case Study in Ferroelectric Materials; 4.4.1 Methods; 4.4.2 Prediction Intervals
  • 4.5 Application of Bayesian Inference to Full Pattern Crystallographic Structure Refinement: A Case Study4.5.1 Data Collection and the Rietveld Analysis; 4.5.2 Importance of Modelling the Variance and Correlation of Residuals; 4.5.3 Bayesian Analysis of the NIST Silicon Standard; 4.5.4 Comparison of the Structure Refinement Approaches; 4.5.5 Programs; 4.6 Conclusion; References; 5 Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials; 5.1 Introduction; 5.2 Case Study 1. Interplay Between Different Structural Order Parameters in Molecular Self-assembly
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319994642
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-99465-9
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783319994659
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1054129034
  • (OCoLC)1054129034
Label
Materials Discovery and Design : By Means of Data Science and Optimal Learning, Turab Lookman, Stephan Eidenbenz, Frank Alexander, Cris Barnes, editors
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Contents; Contributors; 1 Dimensions, Bits, and Wows in Accelerating Materials Discovery; 1.1 Introduction; 1.2 Creativity and Discovery; 1.3 Discovering Dimensions; 1.4 Infotaxis; 1.5 Pursuit of Bayesian Surprise; 1.6 Conclusion; References; 2 Is Automated Materials Design and Discovery Possible?; 2.1 Model Determination in Materials Science; 2.1.1 The Status Quo; 2.1.2 The Goal; 2.2 Identification of the Research and Issues; 2.2.1 Reducing the Degrees of Freedom in Model Determination; 2.2.2 OUQ and mystic; 2.3 Introduction to Uncertainty Quantification
  • 2.3.1 The UQ Problem2.4 Generalizations and Comparisons; 2.4.1 Prediction, Extrapolation, Verification and Validation; 2.4.2 Comparisons with Other UQ Methods; 2.5 Optimal Uncertainty Quantification; 2.5.1 First Description; 2.6 The Optimal UQ Problem; 2.6.1 From Theory to Computation; 2.7 Optimal Design; 2.7.1 The Optimal UQ Loop; 2.8 Model-Form Uncertainty; 2.8.1 Optimal UQ and Model Error; 2.8.2 Game-Theoretic Formulation and Model Error; 2.9 Design and Decision-Making Under Uncertainty; 2.9.1 Optimal UQ for Vulnerability Identification; 2.9.2 Data Collection for Design Optimization
  • 2.10 A Software Framework for Optimization and UQ in Reduced Search Space2.10.1 Optimization and UQ; 2.10.2 A Highly-Configurable Optimization Framework; 2.10.3 Reduction of Search Space; 2.10.4 New Massively-Parallel Optimization Algorithms; 2.10.5 Probability and Uncertainty Tooklit; 2.11 Scalability; 2.11.1 Scalability Through Asynchronous Parallel Computing; References; 3 Importance of Feature Selection in Machine Learning and Adaptive Design for Materials; 3.1 Introduction; 3.2 Computational Details; 3.2.1 Density Functional Theory; 3.2.2 Machine Learning; 3.2.3 Design; 3.3 Results
  • 3.4 Discussion3.5 Summary; References; 4 Bayesian Approaches to Uncertainty Quantification and Structure Refinement from X-Ray Diffraction; 4.1 Introduction; 4.2 Classical Methods of Structure Refinement; 4.2.1 Classical Single Peak Fitting; 4.2.2 The Rietveld Method; 4.2.3 Frequentist Inference and Its Limitations; 4.3 Bayesian Inference; 4.3.1 Sampling Algorithms; 4.4 Application of Bayesian Inference to Single Peak Fitting: A Case Study in Ferroelectric Materials; 4.4.1 Methods; 4.4.2 Prediction Intervals
  • 4.5 Application of Bayesian Inference to Full Pattern Crystallographic Structure Refinement: A Case Study4.5.1 Data Collection and the Rietveld Analysis; 4.5.2 Importance of Modelling the Variance and Correlation of Residuals; 4.5.3 Bayesian Analysis of the NIST Silicon Standard; 4.5.4 Comparison of the Structure Refinement Approaches; 4.5.5 Programs; 4.6 Conclusion; References; 5 Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials; 5.1 Introduction; 5.2 Case Study 1. Interplay Between Different Structural Order Parameters in Molecular Self-assembly
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319994642
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-99465-9
http://library.link/vocab/ext/overdrive/overdriveId
com.springer.onix.9783319994659
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1054129034
  • (OCoLC)1054129034

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