Activity Report

1. Descriptors for Machine Learning of Materials Data

Atsuto Seko, Atsushi Togo and Isao Tanaka
2. Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process Model

Kazuaki Toyoura and Ichiro Takeuchi
3. Machine Learning Predictions of Factors Affecting the Activity of Heterogeneous Metal Catalysts

Ichigaku Takigawa, Ken-ichi Shimizu, Koji Tsuda and Satoru Takakusagi
4. Machine Learning-Based Experimental Design in Materials Science

Thaer M. Dieb and Koji Tsuda
5. Persistent Homology and Materials Informatics

Mickaël Buchet, Yasuaki Hiraoka and Ippei Obayashi
6. Polyhedron and Polychoron Codes for Describing Atomic Arrangements

Kengo Nishio and Takehide Miyazaki

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

Tianmu Zhang, Scott R. Broderick and Krishna Rajan
8. Atomic-Scale Nanostructures by Advanced Electron Microscopy and Informatics

Teruyasu Mizoguchi, Shin Kiyohara, Yuichi Ikuhara and Naoya Shibata
9. High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques

Motoki Shiga and Shunsuke Muto

10. Fabrication, Characterization, and Modulation of Functional Nanolayers

Hiromichi Ohta and Hidenori Hiramatsu
11. Grain Boundary Engineering of Alumina Ceramics

Satoshi Kitaoka, Tsuneaki Matsudaira, Takafumi Ogawa, Naoya Shibata, Miyuki Takeuchi and Yuichi Ikuhara
12. Structural Relaxation of Oxide Compounds from the High-Pressure Phase

Hitoshi Yusa
13. Synthesis and Structures of Novel Solid-State Electrolytes

Ryoji Kanno, Genki Kobayashi, Kota Suzuki, Masaaki Hirayama, Daisuke Mori and Kazuhisa Tamura