Statistical methods for materials science: the data science of microstructure characterization

537 Pages·2019·113.808 MB·English

by  Bouman, Charles Addison, De Graef, Marc, Drummy, Lawrence F., Simmons, Jeffrey P

Statistical Methods for
Materials Science
The Data Science of
Microstructure Characterization

Statistical Methods for
Materials Science
The Data Science of Microstructure
Characterization
Jeffrey P. Simmons
Lawrence F. Drummy
Charles A. Bouman
Marc De Graef
Disclaimer
The views presented by Drs. Simmons and Drummy are theirs alone and do not necessarily represent the views of the
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Library of Congress Cataloging-in-Publication Data
Names: Simmons, Jeffrey P., editor. | Bouman, Charles Addison, editor. | De
Graef, Marc, editor. | Drummy, Lawrence F., editor.
Title: Statistical methods for materials science : the data science of
microstructure characterization / edited by Jeffrey P. Simmons, Charles A.
Bouman, Marc De Graef, Lawrence F. Drummy, Jr.
Description: Boca Raton, Florida : CRC Press, [2019] | Includes
bibliographical references.
Identifiers: LCCN 2018029225| ISBN 9781498738200 (hardback : alk. paper) |
ISBN 9781498738217 (ebook adobe reader) | ISBN 9781351647380 (ebook epub)
| ISBN 9781351637879 (ebook mobipocket)
Subjects: LCSH: Materials science--Mathematical models. | Materials
science--Statistical methods.
Classification: LCC TA404.3 .S77 2019 | DDC 620.1/10727--dc23
LC record available at https://lccn.loc.gov/2018029225
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Contents
Preface xvii
AbouttheEditors xix
Contributors xxi
I Introduction 1
1 MaterialsSciencevs.DataScience 3
by Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman,
and Marc De Graef
II EmergingDataScienceinMicrostructureCharacterization 13
2 EmergingDigitalDataCapabilities 17
byStephenMick
2.1 Introduction 17
2.2 BenefitsofLargeDataVolumes 19
2.3 ChallengesofLargeDataVolumes 21
2.4 EmergingTechniques 22
2.4.1 Multi-InstrumentCoordination 23
2.4.2 UpstreamDataAnalysis 23
2.4.3 DataMining 24
2.4.4 DataCuration 24
2.5 Conclusions 25
3 CulturalDifferences 27
by Mary Comer, Charles A. Bouman, and Jeffrey P. Simmons
3.1 What Makes Modern Image Processing So Modern? 27
3.2 Language of Image Processing 28
3.2.1 NotationalDifferences 28
3.2.1.1 Sets 28
3.2.1.2 OperationsonSets 29
3.2.1.3 ComputationsonSets 31
3.2.2 BayesianProbabilityandImageProcessing 32
3.2.2.1 ModernProbabilityandSets 33
3.2.2.2 FoundationalRulesofModernProbability 33
3.2.2.3 MathematicalConstructs 35
3.2.2.4 BayesianProbabilityinImageProcessing 36
vii
viii Contents
3.3 LanguageofMaterialsScience 39
3.3.1 ThermodynamicPhases 39
3.3.2 FreeEnergies 42
3.4 ConcludingRemarks 46
4 ForwardModeling 47
byMarcDeGraef
4.1 WhatIsForwardModeling? 47
4.1.1 WhatAretheUnknownsinMaterialsCharacterization? 47
4.1.2 ASchematicDescriptionofForwardModeling 49
4.2 ABriefOverviewofElectronScatteringModalities 51
4.3 CaseStudies 52
4.3.1 ElectronBackscatterDiffraction 52
4.3.1.1 BSEMonteCarloSimulations 52
4.3.1.2 DynamicalScatteringSimulations 54
4.3.1.3 DetectorParameters 55
4.3.2 LorentzVectorFieldElectronTomography 56
4.3.2.1 LorentzForwardModel 56
4.3.2.2 ElectronWavePhaseShiftComputations 57
4.3.2.3 ExampleLorentzImageSimulation 61
4.4 Summary 62
5 InverseProblemsandSensing 63
byCharlesA.Bouman
5.1 Introduction 63
5.2 TraditionalApproachestoInversion 63
5.3 BayesianandRegularizedApproachestoInversion 67
5.4 WhyDoesBayesianEstimationWork? 71
5.5 Model-BasedReconstruction 75
5.6 SuccessesandOpportunitiesofBayesianInversion 77
III InverseMethodsforAnalysisofData 81
6 Model-BasedIterativeReconstructionforElectronTomography 85
by Singanallur Venkatakrishnan and Lawrence F. Drummy
6.1 Introduction 85
6.2 Model-Based Iterative Reconstruction 86
6.3 High-Angle Annular Dark-Field STEM Tomography 88
6.3.1 HAADF-STEMForwardModel 88
6.3.2 PriorModel 90
6.3.3 CostFunctionFormulationandOptimizationAlgorithm 91
6.3.4 ExperimentalResults 93
6.3.4.1 SimulatedDataset 93
6.3.4.2 ExperimentalDataset 95
6.4 Bright-FieldElectronTomography 96
6.4.1 BF-TEMForwardModelandCostFunctionFormulation 97
6.4.1.1 GeneralizedHuberFunctionsforAnomalyModeling 98
6.4.1.2 MBIRCostFormulation 100
6.4.2 Results 100
Contents ix
6.4.2.1 SimulatedDataset 101
6.4.2.2 RealDataset 105
6.5 FutureDirections 107
6.6 Conclusion 108
7 Statistical Reconstruction and Heterogeneity Characterization in 3-D Biological
Macromolecular Complexes 111
by Qiu Wang and Peter C. Doerschuk
7.1 Introduction 111
7.2 Statistical 3-D Signal Reconstruction of Macromolecular Complexes 113
7.2.1 Introduction 113
7.2.2 StatisticalModel 113
7.2.3 RelationshipbetweentheMomentsoftheWeightsandtheMomentsof
theElectronScatteringIntensity 115
7.2.4 EstimationCriterion 115
7.2.4.1 qasaFunctionof c¯, V, Q 116
0 0 0
7.2.4.2 c¯,V,andQasaFunctionof c¯, V, Q 116
0 0 0
7.2.4.3 c¯asaFunctionofV,Q, c¯, V, Q 117
0 0 0
7.2.4.4 V asaFunctionofc¯,Q, c¯, V, Q 117
0 0 0
7.2.4.5 QasaFunctionofV,c¯, c¯, V, Q 118
0 0 0
7.2.5 RelationshipwithOtherResults 118
7.2.6 Algorithm 118
7.2.7 Performance 118
7.2.8 Estimation of the a priori Probability Distribution on the Nuisance
Parameters 119
7.2.9 Pre-andPost-Processing 119
7.3 BiologicalExamples 120
7.3.1 FlockHouseVirus(FHV) 120
7.3.2 NudaureliaCapensisω Virus(NωV) 121
7.3.3 Summary 122
7.4 Discussion 123
7.4.1 ChallengesandFutureDirections 123
7.5 Conclusion 125
8 ObjectTrackingthroughImageSequences 127
by Song Wang, Hongkai Yu, Youjie Zhou, Jeffrey P. Simmons, and
Craig Przybyla
8.1 TrackingandKalmanFilters 128
8.2 FiberTrackingUsingtheKalmanFilter 129
8.2.1 FiberDetection 130
8.2.2 ModelParameters 131
8.2.3 MultipleFiberAssociation 131
8.3 TrackingPerformanceEvaluation 133
8.4 TestingDataandSparseSampling 136
8.5 ExperimentResults 137
8.6 OtherTrackingMethods 141
8.7 Summary 141