書籍詳細

書籍詳細




洋書

材料情報学

Materials Informatics : Methods, Tools, and Applications

Curtarolo, Stefano(EDT)

Wiley-Vch 2019/10
320 p. 254 x 173 x   
装丁: Hrd    装丁について
出版国: US
ISBN: 9783527341214
KCN: 1031439532
紀伊國屋書店 選定タイトル
標準価格:¥22,110(本体 ¥20,100)   
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納期について
KDC: F560 材料工学一般
関連書リスト: SB3098D ワイリー 化学特集 2020
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Annotation

Bridging the gap between materials science and informatics, this book introduces readers to up-to-date data mining and machine learning methods.

Full Description

Provides everything readers need to know for applying the power of informatics to materials science There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials. Materials Informatics: Methods, Tools and Applications is presented in two parts?Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others. -Bridges the gap between materials science and informatics -Covers all the known methodologies and applications of materials informatics -Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials -Examines the state-of-the-art software and tools being used today Materials Informatics: Methods, Tools and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics.

Table of Contents

1 Crystallography Open Database: History, Development, and Perspectives 1 Saulius Gra ulis, Andrius Merkys, Antanas Vaitkus, Daniel Chateigner, Luca Lutterotti, Peter Moeck, Miguel Quiros, Robert T. Downs, Werner Kaminsky, and Armel Le Bail 1.1 Introduction 1 1.2 Open Databases for Science 3 1.3 Building COD 6 1.3.1 Scope and Contents 7 1.3.2 Data Sources 7 1.3.3 Data Maintenance 8 1.3.3.1 Version Control 11 1.3.3.2 Data Curation Policies 12 1.3.3.3 Quarterly Releases 13 1.3.4 Sister Databases (PCOD, TCOD) 14 1.4 Use of COD 14 1.4.1 Data Search and Retrieval 14 1.4.1.1 Data Identification 15 1.4.1.2 Web Search Interface 15 1.4.1.3 RESTful Interfaces 15 1.4.1.4 Output Formats 17 1.4.1.5 Accessing COD Records 17 1.4.1.6 MySQL Interface 18 1.4.1.7 Alternative Implementations of COD Search on the Web 20 1.4.1.8 Installing a Local Copy of the COD 21 1.4.1.9 File System-Based Queries 23 1.4.1.10 Programmatic Use of COD CIFs 24 1.4.2 Data Deposition 26 1.5 Applications 27 1.5.1 Material Identification 27 1.5.2 Applications for the Mining Industry 27 1.5.3 Extracting Chemical Information 28 1.5.4 Property Search 30 1.5.5 Geometry Statistics 30 1.5.6 High-Throughput Computations 31 1.5.7 Applications in College Education and Complementing Outreach Activities 31 1.6 Perspectives 32 1.6.1 Historic Structures 32 1.6.2 Theoretical Data in (T)COD 32 1.6.3 Conclusion 32 Acknowledgments 33 References 33 2 The Inorganic Crystal Structure Database (ICSD): A Tool for Materials Sciences 41 Stephan Ruhl 2.1 Introduction 41 2.2 Content of ICSD 42 2.3 Interfaces 46 2.4 Applications of ICSD 46 2.4.1 Prediction of Ferroelectricity 47 2.4.2 Using the Concept of Structure Types 47 2.4.3 Two Examples of Training Machine Learning Algorithms with ICSD Data 48 2.4.4 High-Throughput Calculation 50 2.5 Outlook 51 References 51 3 Pauling File: Toward a Holistic View 55 Pierre Villars, Karin Cenzual, Roman Gladyshevskii, and Shuichi Iwata 3.1 Introduction 55 3.1.1 Creation and Development of the PAULING FILE 57 3.2 PAULING FILE: Crystal Structures 57 3.2.1 Data Selection 58 3.2.2 Categories of Crystal Structure Entries 58 3.2.3 Database Fields 59 3.2.4 Structure Prototypes 62 3.2.5 Standardized Crystallographic Data 63 3.2.5.1 Checking of Symmetry 63 3.2.5.2 Standardization 65 3.2.5.3 Comparison with the Type-Defining Data Set 67 3.2.6 Assigned Atom Coordinates 67 3.2.7 Atomic Environment Types (AETs) 68 3.2.8 Cell Parameters from Plots 72 3.3 PAULING FILE: Phase Diagrams 72 3.4 PAULING FILE: Physical Properties 75 3.4.1 Data Selection 75 3.4.2 Database Fields 76 3.4.3 Physical Properties Considered in the PAULING FILE 76 3.5 Data Quality 80 3.5.1 Computer-Aided Checking 80 3.6 Distinct Phases 81 3.6.1 Chemical Formulas and Phase Names 83 3.6.2 Phase Classifications 84 3.7 Toward a Megadatabase 84 3.8 Applications 89 3.8.1 Products Containing PAULING FILE Data 89 3.8.2 Holistic Overviews Based on the PAULING FILE 91 3.8.3 Principles Defining Ordering of Chemical Elements 92 3.9 Lessons to Learn from Experience 99 3.10 Conclusion 103 References 104 4 From Topological Descriptors to Expert Systems: A Route to Predictable Materials 107 Alexander P. Shevchenko, Eugeny V. Alexandrov, Olga A. Blatova, Denis E. Yablokov, and Vladislav A. Blatov 4.1 Introduction 107 4.2 Topological Tools for Developing Knowledge Databases 108 4.2.1 Why Topological? 108 4.2.2 Topological vs. Other Descriptors of Crystal Structures 110 4.2.3 Topological vs. Crystallographic Databases 111 4.2.4 Deriving Topological Knowledge from Crystallographic Data 116 4.2.4.1 Algorithms for Topological Analysis 116 4.2.4.2 Building Distributions of Descriptors 118 4.2.4.3 Finding Correlations Between Descriptors 123 4.2.5 Universal Data Storage 126 4.3 Applications of Topological Tools in Crystal Chemistry and Materials Science 131 4.3.1 Network Topology Prediction 131 4.3.2 Prediction of Properties 137 4.4 Conclusions 137 References 138 5 A High-Throughput Computational Study Driven by the AiiDA Materials Informatics Framework and the PAULING FILE as Reference Database 149 Martin Uhrin, Giovanni Pizzi, Nicolas Mounet, NicolaMarzari, and Pierre Villars 5.1 Introduction 149 5.1.1 Three Key Developments Opened Up Unprecedented Opportunities 150 5.1.2 Relative Few Inorganic Solids Have Been Experimentally Investigated 151 5.2 Nature Defines Cornerstones Providing a Marvelously Rich but Still Very Rigid Systematic Framework of Restraint Conditions 151 5.3 The First, Second, andThird Paradigms 153 5.4 The Realization of the Fourth and Fifth Paradigms Requires Three Preconditions 153 5.4.1 Introduction of the Prototype Classification to Link Crystallographic Databases Created by Different Groups 153 5.4.2 Introduction of the Distinct Phases Concept to Link Different Kinds of Inorganic Solids Data 154 5.4.3 The Existence of a Comprehensive, Critically Evaluated Inorganic Solids Database Concept (DBMS) of Experimentally Determined Single-Phase Inorganic Solids Data to Be Used as Reference 154 5.5 The Core Idea of the Fifth Paradigm 154 5.6 Restraint Conditions Revealed by "Inorganic Solids Overview-Governing Factor Spaces (Maps)" Discovered by Data-Mining Techniques 156 5.6.1 Compound Formation Maps 157 5.6.2 Atomic Environment Type Stability Maps for AB Inorganic Solids 158 5.6.3 Twelve Principles in Materials Science Supporting Three Cornerstones Given by Nature 159 5.7 Quantum Simulation Strategy 161 5.8 Workflows Engine in AiiDA to Carry Out High-Throughput Calculation for the Creation of the Materials Cloud, Binaries Edition 164 5.8.1 AiiDA 164 5.8.2 SSSP (Standard Solid State Pseudopotentials) Library 165 5.8.3 Workflows 166 5.8.4 Workfunctions 166 5.8.5 Workchains 166 5.8.6 Workflows Used in This Project 168 5.9 Conclusions 169 Acknowledgment 169 References 169 6 Modeling Materials Quantum Properties with Machine Learning 171 Felix A. Faber and O. Anatole von Lilienfeld 6.1 Introduction 171 6.2 Kernel Ridge Regression 171 6.3 Model Assessment 173 6.3.1 Learning Curve 173 6.3.2 Speedup 174 6.4 Representations 176 6.5 Recent Developments 177 References 178 7 Automated Computation of Materials Properties 181 Cormac Toher, Corey Oses, and Stefano Curtarolo 7.1 Introduction 181 7.2 Automated Computational Materials Design Frameworks 182 7.2.1 Generating and Using Databases for Materials Discovery 182 7.2.2 Standardized Protocols for Automated Data Generation 185 7.3 Integrated Calculation of Materials Properties 187 7.3.1 Autonomous Symmetry Analysis 189 7.3.2 Elastic Constants 191 7.3.3 Quasi-harmonic Debye-Gruneisen Model 193 7.3.4 Harmonic Phonons 195 7.3.5 Quasi-harmonic Phonons 197 7.3.6 Anharmonic Phonons 198 7.4 Online Data Repositories 198 7.4.1 Computational Materials Data Web Portals 198 7.4.2 Programmatically Accessible Online Repositories of Computed Materials Properties 200 7.5 Materials Applications 202 7.5.1 Disordered Materials 202 7.5.1.1 High Entropy Materials 203 7.5.1.2 Metallic Glasses 203 7.5.1.3 Modeling Off-Stoichiometry Materials 204 7.5.2 Superalloys 205 7.5.3 Thermoelectrics 205 7.5.4 Magnetic Materials 208 7.6 Conclusion 209 Acknowledgments 209 References 209 8 Cognitive Chemistry: The Marriage of Machine Learning and Chemistry to Accelerate Materials Discovery 223 Edward O. Pyzer-Knapp 8.1 Introduction 223 8.2 Describing Molecules for Machine Learning Algorithms 224 8.3 Building Fast and Accurate Models with Machine Learning 234 8.3.1 Squared Exponential Kernel 239 8.3.2 Rational Quadratic Kernel 240 8.4 Searching Through Chemical Libraries 244 8.5 Conclusion 248 References 249 9 Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation 253 Ivan A. Kruglov, Pavel E. Dolgirev, Artem R. Oganov, Arslan B. Mazitov, Sergey N. Pozdnyakov, Efim A. Mazhnik, and Alexey V. Yanilkin 9.1 Introduction 253 9.2 Machine Learning Potential for Global Optimization 258 9.2.1 Lattice Sums Method 258 9.2.2 Feature Vector 261 9.2.3 Feature Vector Analysis 262 9.2.4 Examples of Machine Learning Interatomic Potentials 265 9.2.4.1 Aluminum 265 9.2.4.2 Carbon 267 9.2.4.3 Helium and Xenon 271 9.2.5 Discussion 272 9.3 Interatomic Potential for Molecular Dynamics 273 9.3.1 General Form of the Potential 273 9.3.2 Parameters Selection 274 9.3.3 Thermodynamic Quantities and Phase Transitions 277 9.3.4 Interatomic Potential for System of Two (or More) Atomic Types 281 9.4 Statistical Approach for Constructing ML Potentials 284 9.4.1 Two-Body Potential 284 9.4.2 Three-Body Potential 286 Acknowledgements 286 References 286 Index 289