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|a 620.115
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|a Tanaka, Isao.
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|a Nanoinformatics.
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|a 1st ed.
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|a Singapore :
|b Springer Singapore Pte. Limited,
|c 2018.
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|c ©2018.
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|a 1 online resource (296 pages)
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|a text
|b txt
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|a 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.
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|a 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)}.
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|a 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.
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|a 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.
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|a 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.
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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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|i Print version:
|a Tanaka, Isao
|t Nanoinformatics
|d Singapore : Springer Singapore Pte. Limited,c2018
|z 9789811076169
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| 797 |
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|a ProQuest (Firm)
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6422634
|z Click to View
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