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Isao Tanaka Editor Nanoinformatics Nanoinformatics Isao Tanaka Editor Isao Tanaka Editor Nanoinformatics Nanoinformatics Isao Tanaka Editor Nanoinformatics 123 Editor Isao Tanaka Kyoto University Kyoto Japan ISBN 978-981-10-7616-9 ISBN 978-981-10-7617-6 (eBook) https://doi.org/10.1007/978-981-10-7617-6 Library of Congress Control Number: 2017960908 © The Editor(s) (if applicable) and The Author(s) 2018. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publi- cation does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Preface This book focuses on state-of-the-art ideas and tools in informatics that are cur- rently being used in materials science, or are expected to be used in the future. Collaborative research between materials science and information science is growing actively, creating new trends in materials science and engineering. Areas utilizing “big data,” generated by experiments and computations to accelerate the discovery of new materials, key factors, and design rules, have rapidly progressed. Data-intensive approaches are indispensable in advanced materials characterization. “Material informatics” is the central paradigm in this new trend. An essential subset is “nanoinformatics,” which focuses on the nanostructures of materials, such as surfaces, interfaces, dopants, and point defects. Experimental and computational techniques to characterize and gain quantitative information about nanostructures have significantly advanced, enabling nanoinformatics to play a critical role in determining material properties. Most of this book is derived from the collaborative research projects supported by the Grant-in-Aid for Scientific Research on Innovative Areas “Nano Informatics” from the Japan Society for the Promotion of Science (JSPS). This five-year project, which was launched in 2013, aims to accelerate the exploration of frontiers in materials science and promote the integration of information and uti- lization of accumulated knowledge regarding nanostructures for the design and innovation of actual materials. Project researchers represent diverse disciplines, such as materials science, applied physics, solid-state chemistry, catalytic chem- istry, and information science. In addition to those working in the collaborative program, three research groups actively working on data-centric materials science were invited to contribute to the book. With their participation, the subjects in the book are well balanced. This book is composed of three parts. The first part reviews the ideas and tools of materials informatics as well as actual applications of machine-learning techniques for materials problems. Chapter 1 shows how compounds in materials datasets can be represented as descriptors and applied to machine-learning models. Chapter 2 focuses on a method to discover the potential energy surface of solid-state ionic conductors via a combination of first principles calculations and machine-learning v vi Preface techniques. Chapter 3 describes the machine-learning predictions of factors affecting the activity of heterogeneous metal catalysts. Chapter 4 discusses the applications of optimal experimental design algorithms for materials science. Chapters 5 and 6 are dedicated to the topological analyses of the atomic structure data of materials. One method is called persistent homology. The other uses polyhedron and polychoron codes. They have been successfully used to analyze amorphous structures. In the second part, data-centric approaches used for nanoscale analyses of materials data are described. Chapter 7 shows topological data analyses for atom probe tomography (APT) images. Chapter 8 describes the combined efforts of scanning transmission electron microscopy (STEM) experiments, first principles calculations, and informatics approaches to analyzing the atomic structures of materials interfaces. Chapter 9 is based on nanoscale STEM spectroscopic datasets that are analyzed by machine-learning techniques. The third part is composed of four chapters. Each chapter focuses on a specific target of nanoinformatics approaches. Chapter 10 describes high-quality epitaxial films of materials called “nanolayers” for a variety of functional applications, including thermoelectrics, batteries, memories, and superconductors. Chapter 11 focuses on the grain boundary engineering of alumina ceramics for use as protective films in the hot-section components of airplane engines, gas turbines, and heat treatment furnaces in combustion environments. Chapter 12 shows the structural relaxation of high-pressure oxide compounds, which is important for quenching high-pressure phases in ambient conditions. Chapter 13 describes the syntheses and structures of novel lithium-ion and hydride-ion conductors for use as solid-state electrolytes in electrochemical devices. This book is an efficient overview of current progress in emerging and inter- disciplinary research areas. It will benefit experimentalists and theorists in both academic and industry sectors. All the authors and steering committee members of the collaborative program “Nano Informatics” are gratefully acknowledged. Without their devoted efforts, this book would not be possible. Financial support for the open access publication of this book by a Grant-in-Aid for Scientific Research on Innovative Areas “Nano Informatics” (Grant No. 25106001) from the JSPS is gratefully acknowledged. Kyoto, Japan Isao Tanaka Contents Part I Materials Informatics 1 Descriptors for Machine Learning of Materials Data ........... 3 Atsuto Seko, Atsushi Togo and Isao Tanaka 2 Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process Model ........ 25 Kazuaki Toyoura and Ichiro Takeuchi 3 Machine Learning Predictions of Factors Affecting the Activity of Heterogeneous Metal Catalysts ................ 45 Ichigaku Takigawa, Ken-ichi Shimizu, Koji Tsuda and Satoru Takakusagi 4 Machine Learning-Based Experimental Design in Materials Science .................................... 65 Thaer M. Dieb and Koji Tsuda 5 Persistent Homology and Materials Informatics ............... 75 Mickaël Buchet, Yasuaki Hiraoka and Ippei Obayashi 6 Polyhedron and Polychoron Codes for Describing Atomic Arrangements ......................................... 97 Kengo Nishio and Takehide Miyazaki Part II Nanoscale Analyses and Informatics 7 Topological Data Analysis for the Characterization of Atomic Scale Morphology from Atom Probe Tomography Images ....... 133 Tianmu Zhang, Scott R. Broderick and Krishna Rajan 8 Atomic-Scale Nanostructures by Advanced Electron Microscopy and Informatics .............................. 157 Teruyasu Mizoguchi, Shin Kiyohara, Yuichi Ikuhara and Naoya Shibata vii viii Contents 9 High Spatial Resolution Hyperspectral Imaging with Machine-Learning Techniques ........................ 179 Motoki Shiga and Shunsuke Muto Part III Materials Developments 10 Fabrication, Characterization, and Modulation of Functional Nanolayers ................................ 207 Hiromichi Ohta and Hidenori Hiramatsu 11 Grain Boundary Engineering of Alumina Ceramics ............ 237 Satoshi Kitaoka, Tsuneaki Matsudaira, Takafumi Ogawa, Naoya Shibata, Miyuki Takeuchi and Yuichi Ikuhara 12 Structural Relaxation of Oxide Compounds from the High-Pressure Phase ............................. 259 Hitoshi Yusa 13 Synthesis and Structures of Novel Solid-State Electrolytes ....... 279 Ryoji Kanno, Genki Kobayashi, Kota Suzuki, Masaaki Hirayama, Daisuke Mori and Kazuhisa Tamura Part I Materials Informatics Chapter 1 Descriptors for Machine Learning of Materials Data Atsuto Seko, Atsushi Togo and Isao Tanaka Abstract Descriptors, which are representations of compounds, play an essential role in machine learning of materials data. Although many representations of ele- ments and structures of compounds are known, these representations are difficult to use as descriptors in
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