Linked e-resources
Details
Table of 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.
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.