Lookman / Eidenbenz / Alexander Materials Discovery and Design

By Means of Data Science and Optimal Learning
1. Auflage 2018
ISBN: 978-3-319-99465-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 280, 266 Seiten

Reihe: Springer Series in Materials Science

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. 

Autoren/Hrsg.

Lookman, Turab

Eidenbenz, Stephan

Alexander, Frank

Barnes, Cris

Inhaltsverzeichnis

1;Preface;6
2;Contents;9
3;Contributors;14
4;1 Dimensions, Bits, and Wows in Accelerating Materials Discovery;16
4.1;1.1 Introduction;16
4.2;1.2 Creativity and Discovery;18
4.3;1.3 Discovering Dimensions;20
4.4;1.4 Infotaxis;21
4.5;1.5 Pursuit of Bayesian Surprise;23
4.6;1.6 Conclusion;26
4.7;References;26
5;2 Is Automated Materials Design and Discovery Possible?;30
5.1;2.1 Model Determination in Materials Science;31
5.1.1;2.1.1 The Status Quo;31
5.1.2;2.1.2 The Goal;31
5.2;2.2 Identification of the Research and Issues;32
5.2.1;2.2.1 Reducing the Degrees of Freedom in Model Determination;32
5.2.2;2.2.2 OUQ and mystic;34
5.3;2.3 Introduction to Uncertainty Quantification;36
5.3.1;2.3.1 The UQ Problem;36
5.4;2.4 Generalizations and Comparisons;39
5.4.1;2.4.1 Prediction, Extrapolation, Verification and Validation;39
5.4.2;2.4.2 Comparisons with Other UQ Methods;40
5.5;2.5 Optimal Uncertainty Quantification;42
5.5.1;2.5.1 First Description;43
5.6;2.6 The Optimal UQ Problem;46
5.6.1;2.6.1 From Theory to Computation;46
5.7;2.7 Optimal Design;51
5.7.1;2.7.1 The Optimal UQ Loop;51
5.8;2.8 Model-Form Uncertainty;55
5.8.1;2.8.1 Optimal UQ and Model Error;55
5.8.2;2.8.2 Game-Theoretic Formulation and Model Error;56
5.9;2.9 Design and Decision-Making Under Uncertainty;57
5.9.1;2.9.1 Optimal UQ for Vulnerability Identification;57
5.9.2;2.9.2 Data Collection for Design Optimization;58
5.10;2.10 A Software Framework for Optimization and UQ in Reduced Search Space;59
5.10.1;2.10.1 Optimization and UQ;59
5.10.2;2.10.2 A Highly-Configurable Optimization Framework;60
5.10.3;2.10.3 Reduction of Search Space;61
5.10.4;2.10.4 New Massively-Parallel Optimization Algorithms;64
5.10.5;2.10.5 Probability and Uncertainty Tooklit;65
5.11;2.11 Scalability;68
5.11.1;2.11.1 Scalability Through Asynchronous Parallel Computing;68
5.12;References;69
6;3 Importance of Feature Selection in Machine Learning and Adaptive Design for Materials;74
6.1;3.1 Introduction;75
6.2;3.2 Computational Details;77
6.2.1;3.2.1 Density Functional Theory;77
6.2.2;3.2.2 Machine Learning;78
6.2.3;3.2.3 Design;78
6.3;3.3 Results;79
6.4;3.4 Discussion;88
6.5;3.5 Summary;91
6.6;References;92
7;4 Bayesian Approaches to Uncertainty Quantification and Structure Refinement from X-Ray Diffraction;95
7.1;4.1 Introduction;95
7.2;4.2 Classical Methods of Structure Refinement;97
7.2.1;4.2.1 Classical Single Peak Fitting;97
7.2.2;4.2.2 The Rietveld Method;98
7.2.3;4.2.3 Frequentist Inference and Its Limitations;100
7.3;4.3 Bayesian Inference;101
7.3.1;4.3.1 Sampling Algorithms;103
7.4;4.4 Application of Bayesian Inference to Single Peak Fitting: A Case Study in Ferroelectric Materials;104
7.4.1;4.4.1 Methods;106
7.4.2;4.4.2 Prediction Intervals;107
7.5;4.5 Application of Bayesian Inference to Full Pattern Crystallographic Structure Refinement: A Case Study;108
7.5.1;4.5.1 Data Collection and the Rietveld Analysis;109
7.5.2;4.5.2 Importance of Modelling the Variance and Correlation of Residuals;110
7.5.3;4.5.3 Bayesian Analysis of the NIST Silicon Standard;111
7.5.4;4.5.4 Comparison of the Structure Refinement Approaches;111
7.5.5;4.5.5 Programs;113
7.6;4.6 Conclusion;114
7.7;References;115
8;5 Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials;117
8.1;5.1 Introduction;118
8.2;5.2 Case Study 1. Interplay Between Different Structural Order Parameters in Molecular Self-assembly;120
8.2.1;5.2.1 Model System and Problem Overview;120
8.2.2;5.2.2 How to Find Positions of All Molecules in the Image?;121
8.2.3;5.2.3 Identifying Molecular Structural Degrees of Freedom via Computer Vision;122
8.2.4;5.2.4 Application to Real Experimental Data: From Imaging to Physics and Chemistry;126
8.3;5.3 Case Study 2. Role of Lattice Strain in Formation of Electron Scattering Patterns in Graphene;129
8.3.1;5.3.1 Model System and Problem Overview;129
8.3.2;5.3.2 How to Extract Structural and Electronic Degrees of Freedom Directly from an Image?;130
8.3.3;5.3.3 Direct Data Mining of Structure and Electronic Degrees of Freedom in Graphene;131
8.4;5.4 Case Study 3. Correlative Analysis in Multi-mode Imaging of Strongly Correlated Electron Systems;135
8.4.1;5.4.1 Model System and Problem Overview;135
8.4.2;5.4.2 How to Obtain Physically Meaningful Endmembers from Hyperspectral Tunneling Conductance Data?;136
8.5;5.5 Overall Conclusion and Outlook;140
8.6;References;141
9;6 Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization;143
9.1;6.1 Introduction;144
9.2;6.2 In Situ Techniques;147
9.3;6.3 Experimental Rates;150
9.4;6.4 Experimental and Image Acquisition;155
9.5;6.5 Reconstruction;159
9.6;6.6 Visualization;160
9.7;6.7 Segmentation;162
9.8;6.8 Modeling;165
9.9;6.9 In Situ Data;166
9.10;6.10 Analyze and Advanced Processing;167
9.11;6.11 Conclusions;170
9.12;References;172
10;7 Overview of High-Energy X-Ray Diffraction Microscopy (HEDM) for Mesoscale Material Characterization in Three-Dimensions;180
10.1;7.1 Introduction;180
10.1.1;7.1.1 The Mesoscale;181
10.1.2;7.1.2 Imaging Techniques;182
10.2;7.2 Brief Background on Scattering Physics;184
10.2.1;7.2.1 Scattering by an Atom;185
10.2.2;7.2.2 Crystallographic Planes;187
10.2.3;7.2.3 Diffraction by a Small Crystal;188
10.2.4;7.2.4 Electron Density;190
10.3;7.3 High-Energy X-Ray Diffraction Microscopy (HEDM);191
10.3.1;7.3.1 Experimental Setup;191
10.3.2;7.3.2 Data Analysis;192
10.4;7.4 Microstructure Representation;194
10.5;7.5 Example Applications;196
10.5.1;7.5.1 Tracking Plastic Deformation in Polycrystalline Copper Using Nf-HEDM;196
10.5.2;7.5.2 Combined nf- and ff-HEDM for Tracking Inter-granular Stress in Titanium Alloy;199
10.5.3;7.5.3 Tracking Lattice Rotation Change in Interstitial-Free (IF) Steel Using HEDM;200
10.5.4;7.5.4 Grain-Scale Residual Strain (Stress) Determination in Ti-7Al Using HEDM;202
10.5.5;7.5.5 In-Situ ff-HEDM Characterization of Stress-Induced Phase Transformation in Nickel-Titanium Shape Memory Alloys (SMA);203
10.5.6;7.5.6 HEDM Application to Nuclear Fuels;204
10.5.7;7.5.7 Utilizing HEDM to Characterize Additively Manufactured 316L Stainless Steel;205
10.6;7.6 Conclusions and Perspectives;207
10.6.1;7.6.1 Establishing Processing-Structure- Property-Performance Relationships;209
10.7;References;211
11;8 Bragg Coherent Diffraction Imaging Techniques at 3rd and 4th Generation Light Sources;215
11.1;8.1 Introduction;216
11.2;8.2 BCDI Methods at Light Sources;223
11.3;8.3 Big Data Challenges in BCDI;224
11.4;8.4 Conclusions;226
11.5;References;226
12;9 Automatic Tuning and Control for Advanced Light Sources;228
12.1;9.1 Introduction;229
12.1.1;9.1.1 Beam Dynamics;231
12.1.2;9.1.2 RF Acceleration;233
12.1.3;9.1.3 Bunch Compression;234
12.1.4;9.1.4 RF Systems;235
12.1.5;9.1.5 Need for Feedback Control;237
12.1.6;9.1.6 Standart Proportional Integral (PI) Control for RF Cavity;238
12.2;9.2 Advanced Control and Tuning Topics;243
12.3;9.3 Introduction to Extremum Seeking Control;244
12.3.1;9.3.1 Physical Motivation;245
12.3.2;9.3.2 General ES Scheme;247
12.3.3;9.3.3 ES for RF Beam Loading Compensation;249
12.3.4;9.3.4 ES for Magnet Tuning;251
12.3.5;9.3.5 ES for Electron Bunch Longitudinal Phase Space Prediction;253
12.3.6;9.3.6 ES for Phase Space Tuning;257
12.4;9.4 Conclusions;260
12.5;References;260
13;Index;263