Table of Contents: Nanoinformatics.

Nanoinformatics.

Bibliographic Details
Main Author:	Tanaka, Isao.
Format:	eBook
Language:	English
Published:	Singapore : Springer Singapore Pte. Limited, 2018.
Edition:	1st ed.
Subjects:	Electronic books.
Online Access:	Click to View

Table of Contents:

Intro
Preface
Contents
Materials Informatics
1 Descriptors for Machine Learning of Materials Data
1.1 Introduction
1.2 Compound Descriptors
1.3 Elemental Representations
1.4 Structural Representations
1.5 Machine Learning of DFT Cohesive Energy
1.6 Construction of MLIP for Elemental Metals
1.7 Discovery of Low Lattice Thermal Conductivity Materials
1.8 Recommender System Approach for Materials Discovery
References
2 Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process Model
Abstract
2.1 Introduction
2.2 Problem Setup
2.2.1 Entire Proton PES in BaZrO3
2.2.2 Problem Statement
2.3 GP-Based Selective Sampling Procedure
2.3.1 Gaussian Process Models
2.3.2 Selection Criterion
2.3.3 PE Threshold
2.3.4 Termination Criterion
2.4 Results of Selective Sampling
2.4.1 Low-PE Region Identification
2.4.2 Low-FN Region Identification
2.4.3 Practical Issues
2.5 Conclusions
Acknowledgements
References
3 Machine Learning Predictions of Factors Affecting the Activity of Heterogeneous Metal Catalysts
Abstract
3.1 Introduction
3.2 The d-Band Center: A Widely Accepted Indicator Explaining Activity Trends in Metal Catalysts
3.3 Prediction of the d-Band Center Values for Mono- and Bimetallic Systems by Machine Learning
3.3.1 Data-Driven Prediction of d-Band Center Values by Machine Learning Methods
3.3.2 Datasets and Descriptors
3.3.3 Monte Carlo Cross-Validation for Assessing the Prediction Accuracies of ML Models
3.3.4 Machine Learning Methods and Hyperparameter Selection
3.3.5 Screening and Evaluation of Predictive ML Methods
3.3.6 The Importance of Descriptors to GBR Predictions
3.3.7 Model Estimations Using Different Test/Training Splits
3.4 Conclusion and Future Prospects.
Acknowledgements
References
4 Machine Learning-Based Experimental Design in Materials Science
4.1 Introduction
4.2 Bayesian Optimization
4.2.1 Method
4.2.2 COMBO: Bayesian Optimization Package
4.2.3 Designing Phonon Transport Nanostructures
4.3 Monte Carlo Tree Search
4.3.1 Method
4.3.2 MDTS: A Python Package for MCTS
4.3.3 Discussion
4.4 Concluding Remarks
References
5 Persistent Homology and Materials Informatics
5.1 Introduction
5.2 Mathematical Background
5.2.1 Homology
5.2.2 From Point Sets to Simplicial Complexes
5.2.3 Persistent Homology
5.2.4 Computation
5.2.5 Digital Images
5.3 Materials TDA
5.3.1 Silica Glass
5.3.2 Grain Packing
5.3.3 Craze Formation of Polymer
5.4 Discussions
References
6 Polyhedron and Polychoron Codes for Describing Atomic Arrangements
Abstract
6.1 Introduction
6.2 Polyhedron Code
6.2.1 Our Way of Viewing a Polyhedron
6.2.2 Decoding Simple Polyhedra
6.2.2.1 How to Recover a 34443-Polyhedron
6.2.2.2 Polyhedron Codeword
6.2.2.3 Algorithm for Recovering the Original Polyhedron from {\bi p}_{3}
6.2.3 Encoding Simple Polyhedra
6.2.3.1 Schlegel Diagram
6.2.3.2 Polygon-Sequence Codeword
6.2.3.3 Outline of How to Generate {\bi sp}
6.2.3.4 Plot
6.2.3.5 How to Generate {\bi tsp}^{\left( 0 \right)}
6.2.3.6 How to Generate {\bi sp}
6.2.3.7 Lexicographical Number of {\bi p}_{3}
6.2.3.8 Solving the Problem of Voronoi Index
6.2.4 Non-simple Polyhedron
6.2.4.1 Cut-and-Dot Method
6.2.4.2 Using Duality
6.2.5 Relation Between an Atomic Arrangement and a Voronoi Polyhedron
6.3 Polychoron Code
6.3.1 Our Way of Viewing a Polychoron
6.3.2 1-Simple Polychoron
6.3.3 Polychoron Codeword
6.3.4 How to Generate {\bi ps}_{3}
6.3.5 How to Generate {\bi tfp}^{\left( 0 \right)}.
6.3.6 How to Recover a Polychoron from {\bi ps}_{3} \semicolon {\bi tsp}^{\left( 0 \right)}
6.3.7 How to Generate \bi fp
6.3.8 Lexicographical Number of {\bi p}_{4}
6.3.9 Non-1-Simple Polychora
6.3.10 Ridge-Sequence Codeword
6.3.11 Relation Between a Local Atomic Arrangement and an Assemblage of Voronoi Polyhedra
6.4 Summary
References
Nanoscale Analyses and Informatics
7 Topological Data Analysis for the Characterization of Atomic Scale Morphology from Atom Probe Tomography Images
Abstract
7.1 Introduction
7.1.1 Atom Probe Tomography Data and Analysis
7.1.2 Characteristics of Geometric-Based Data Analysis Methods
7.2 Persistent Homology
7.3 Voxel Size Determination: Identification of Interfaces
7.4 Topological Analysis for Defining Morphology of Precipitates
7.5 Spatial Uncertainty in Isosurfaces
7.6 Summary
Acknowledgements
References
8 Atomic-Scale Nanostructures by Advanced Electron Microscopy and Informatics
Abstract
8.1 Atomic Structures of Interfaces
8.2 Informatics Approach for Interfaces
8.2.1 Virtual Screening
8.2.2 Bayesian Optimization (Kriging) [15]
8.2.3 Kriging Method for Oxide Interfaces [16]
8.3 Microscopic Approach for Interfaces
8.3.1 Scanning Transmission Electron Microscopy (STEM)
8.3.2 Interface Structures Using Aberration-Corrected STEM
8.3.2.1 Solute Segregation Behavior of a ∑3 Grain Boundary in Yttria Stabilized Zirconia [39]
8.3.2.2 Dopant Segregation Behavior in a Metal/Ceramic Interface [40]
Acknowledgements
References
9 High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques
Abstract
9.1 Introduction
9.2 Methodology
9.2.1 Mathematical Formulation of HSI Data
9.2.2 Non-negative Matrix Factorization with a Gaussian Noise Model.
9.2.3 Optimization Algorithms with Soft Spatial Orthogonal Constraint
9.2.4 Probabilistic View of a NMF Model with an Automatic Relevance Determination Prior
9.2.5 Optimization Algorithm for C with Both ARD and Spatial Orthogonal Constraint
9.3 Application
9.3.1 Experimental Procedures
9.3.2 Spatial Orthogonal Constraint on STEM-EELS Data
9.3.2.1 XSTEM-EELS Data from a Silicon Device
9.3.2.2 Atomic Resolution STEM-EELS of Mn3O4
9.3.3 Results of Optimizing the Number of Components by ARD-NMF
9.3.3.1 STEM-EDX Data
9.3.3.2 XSTEM-EELS Data from a Silicon Device
9.3.3.3 Atomic Resolution STEM-EELS of Mn3O4
9.4 Discussion
9.5 Summary
Acknowledgements
References
Materials Developments
10 Fabrication, Characterization, and Modulation of Functional Nanolayers
Abstract
10.1 Epitaxial Growth and Characterization of Functional Nanolayers
10.2 Pulsed Laser Deposition
10.3 Reactive Solid-Phase Epitaxy
10.3.1 Na≈2/3MnO2 Epitaxial Film
10.3.2 Li4Ti5O12 Epitaxial Film
10.3.3 KFe2As2 Epitaxial Film
10.3.4 (Sn, Pb)Se Epitaxial Film
10.4 Modulation of Functional Nanolayers
10.4.1 Utilizing Antiferromagnetic Insulator/Ferromagnetic Metal Conversion in SrCoO2.5+δ [67]
10.4.2 Utilizing a Colorless Transparent Insulator/Dark Blue Metal Conversion in HxWO3 [68]
Acknowledgements
References
11 Grain Boundary Engineering of Alumina Ceramics
Abstract
11.1 Introduction
11.2 Experimental Procedures
11.2.1 Oxygen Permeability Measurements
11.2.2 Determination of Oxygen GB Diffusion Coefficients for Each GB
11.3 Results and Discussion
11.3.1 Oxygen Permeation
11.3.2 GB Diffusion Under Oxygen Potential Gradients
11.3.3 Design of Oxygen Shielding Capability and Structural Stability
11.3.4 Mass-Transfer in Alumina Scale
11.4 Conclusions
Acknowledgements.
References
12 Structural Relaxation of Oxide Compounds from the High-Pressure Phase
Abstract
12.1 General
12.2 Phase Transition from the Perovskite Structure to the Lithium Niobate Structure
12.2.1 Crystal Structure Relationship Among Lithium Niobate, Perovskite, and Ilmenite Phases
12.2.2 Perovskite Tolerance Factor
12.2.3 Structure Stability from a Computational Viewpoint
12.3 Amorphization from Cubic and Hexagonal Silicate Perovskites
12.3.1 Phase Transition Sequence of Silicate Perovskites
12.3.2 Crystal Structures of Hexagonal Perovskite and Structural Relation with Cubic Perovskite
12.3.3 Phase Diagrams: Experiments and Ab Initio Calculations
12.3.4 Amorphization Under Decompression at Room Temperature
12.4 Relaxation Structures from the High-Pressure Phases of Sesquioxides
12.4.1 Rh2O3(II) Structure Reverting to the Corundum Structure in Group 13 Sesquioxides
12.4.2 A-RES Structure of Y2O3 Reverting to the B-RES Structure
12.5 Concluding Remarks
Acknowledgements
References
13 Synthesis and Structures of Novel Solid-State Electrolytes
Abstract
13.1 Novel Solid-State Electrolytes
13.2 Lithium Ion Conductors
13.2.1 Novel Lithium Ion-Conducting Perovskite Oxides [15]
13.2.2 M-Doped LiScO2 (M = Zr, Nb, Ta) [21] as New Lithium Ion Conductors
13.3 Development of Hydride Ion Conductors
13.3.1 Hydride-Conducting Oxyhydrides La2-X-YSrx+YH1-X+YO3-Y
13.3.2 Hydride Ion Conductivity of La2-X-YSrx+YH1-X+YO3-Y
13.3.3 Development of Electrochemical Devices Based on Hydride Ion Conduction
13.3.4 Ambient-Pressure Synthesis of H
Conductive Oxyhydrides
13.4 Concluding Remarks
Acknowledgements
References.

Nanoinformatics.

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