Nanoinformatics

Chapter 1 Descriptors for Machine Learning of Materials Data

Chapter 2 Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process Model

Chapter 3 Machine Learning Predictions of Factors Affecting the Activity of Heterogeneous Metal Catalysts

Chapter 4 Machine Learning-Based Experimental Design in Materials Science

Chapter 6 Polyhedron and Polychoron Codes for Describing Atomic Arrangements

Chapter 7 Topological Data Analysis for the Characterization of Atomic Scale Morphology from Atom Probe Tomography Images

Chapter 8 Atomic-Scale Nanostructures by Advanced Electron Microscopy and Informatics

Chapter 9 High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Chapter 10 Fabrication, Characterization, and Modulation of Functional Nanolayers

Chapter 11 Grain Boundary Engineering of Alumina Ceramics

Chapter 12 Structural Relaxation of Oxide Compounds from the High-Pressure Phase

Chapter 13 Synthesis and Structures of Novel Solid-State Electrolytes