Note: Cover -- Title Page -- Copyright -- Contents -- Chapter 1 Crystallography Open Database: History, Development, and Perspectives -- 1.1 Introduction -- 1.2 Open Databases for Science -- 1.3 Building COD -- 1.3.1 Scope and Contents -- 1.3.2 Data Sources -- 1.3.3 Data Maintenance -- 1.3.3.1 Version Control -- 1.3.3.2 Data Curation Policies -- 1.3.3.3 Quarterly Releases -- 1.3.4 Sister Databases (PCOD, TCOD) -- 1.4 Use of COD -- 1.4.1 Data Search and Retrieval -- 1.4.1.1 Data Identification -- 1.4.1.2 Web Search Interface -- 1.4.1.3 RESTful Interfaces -- 1.4.1.4 Output Formats -- 1.4.1.5 Accessing COD Records -- 1.4.1.6 MySQL Interface -- 1.4.1.7 Alternative Implementations of COD Search on the Web -- 1.4.1.8 Installing a Local Copy of the COD -- 1.4.1.9 File System‐Based Queries -- 1.4.1.10 Programmatic Use of COD CIFs -- 1.4.2 Data Deposition -- 1.5 Applications -- 1.5.1 Material Identification -- 1.5.2 Applications for the Mining Industry -- 1.5.3 Extracting Chemical Information -- 1.5.4 Property Search -- 1.5.5 Geometry Statistics -- 1.5.6 High‐Throughput Computations -- 1.5.7 Applications in College Education and Complementing Outreach Activities -- 1.6 Perspectives -- 1.6.1 Historic Structures -- 1.6.2 Theoretical Data in (T)COD -- 1.6.3 Conclusion -- Acknowledgments -- References -- Chapter 2 The Inorganic Crystal Structure Database (ICSD): A Tool for Materials Sciences -- 2.1 Introduction -- 2.2 Content of ICSD -- 2.3 Interfaces -- 2.4 Applications of ICSD -- 2.4.1 Prediction of Ferroelectricity -- 2.4.2 Using the Concept of Structure Types -- 2.4.3 Two Examples of Training Machine Learning Algorithms with ICSD Data -- 2.4.4 High‐Throughput Calculation -- 2.5 Outlook -- References -- Chapter 3 Pauling File: Toward a Holistic View -- 3.1 Introduction -- 3.1.1 Creation and Development of the PAULING FILE -- 3.2 PAULING FILE: Crystal Structures. , 3.2.1 Data Selection -- 3.2.2 Categories of Crystal Structure Entries -- 3.2.3 Database Fields -- 3.2.4 Structure Prototypes -- 3.2.5 Standardized Crystallographic Data -- 3.2.5.1 Checking of Symmetry -- 3.2.5.2 Standardization -- 3.2.5.3 Comparison with the Type‐Defining Data Set -- 3.2.6 Assigned Atom Coordinates -- 3.2.7 Atomic Environment Types (AETs) -- 3.2.8 Cell Parameters from Plots -- 3.3 PAULING FILE: Phase Diagrams -- 3.4 PAULING FILE: Physical Properties -- 3.4.1 Data Selection -- 3.4.2 Database Fields -- 3.4.3 Physical Properties Considered in the PAULING FILE -- 3.5 Data Quality -- 3.5.1 Computer‐Aided Checking -- 3.6 Distinct Phases -- 3.6.1 Chemical Formulas and Phase Names -- 3.6.2 Phase Classifications -- 3.7 Toward a Megadatabase -- 3.8 Applications -- 3.8.1 Products Containing PAULING FILE Data -- 3.8.2 Holistic Overviews Based on the PAULING FILE -- 3.8.3 Principles Defining Ordering of Chemical Elements -- 3.9 Lessons to Learn from Experience -- 3.10 Conclusion -- References -- Chapter 4 From Topological Descriptors to Expert Systems: A Route to Predictable Materials -- 4.1 Introduction -- 4.2 Topological Tools for Developing Knowledge Databases -- 4.2.1 Why Topological? -- 4.2.2 Topological vs. Other Descriptors of Crystal Structures -- 4.2.3 Topological vs. Crystallographic Databases -- 4.2.4 Deriving Topological Knowledge from Crystallographic Data -- 4.2.4.1 Algorithms for Topological Analysis -- 4.2.4.2 Building Distributions of Descriptors -- 4.2.4.3 Finding Correlations Between Descriptors -- 4.2.5 Universal Data Storage -- 4.3 Applications of Topological Tools in Crystal Chemistry and Materials Science -- 4.3.1 Network Topology Prediction -- 4.3.2 Prediction of Properties -- 4.4 Conclusions -- References. , Chapter 5 A High‐Throughput Computational Study Driven by the AiiDA Materials Informatics Framework and the PAULING FILE as Reference Database -- 5.1 Introduction -- 5.1.1 Three Key Developments Opened Up Unprecedented Opportunities -- 5.1.2 Relative Few Inorganic Solids Have Been Experimentally Investigated -- 5.2 Nature Defines Cornerstones Providing a Marvelously Rich but Still Very Rigid Systematic Framework of Restraint Conditions -- 5.3 The First, Second, and Third Paradigms -- 5.4 The Realization of the Fourth and Fifth Paradigms Requires Three Preconditions -- 5.4.1 Introduction of the Prototype Classification to Link Crystallographic Databases Created by Different Groups -- 5.4.2 Introduction of the Distinct Phases Concept to Link Different Kinds of Inorganic Solids Data -- 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 -- 5.5 The Core Idea of the Fifth Paradigm -- 5.6 Restraint Conditions Revealed by "Inorganic Solids Overview-Governing Factor Spaces (Maps)" Discovered by Data‐Mining Techniques -- 5.6.1 Compound Formation Maps -- 5.6.2 Atomic Environment Type Stability Maps for AB Inorganic Solids -- 5.6.3 Twelve Principles in Materials Science Supporting Three Cornerstones Given by Nature -- 5.7 Quantum Simulation Strategy -- 5.8 Workflows Engine in AiiDA to Carry Out High‐Throughput Calculation for the Creation of the Materials Cloud, Binaries Edition -- 5.8.1 AiiDA -- 5.8.2 SSSP (Standard Solid State Pseudopotentials) Library -- 5.8.3 Workflows -- 5.8.4 Workfunctions -- 5.8.5 Workchains -- 5.8.6 Workflows Used in This Project -- 5.9 Conclusions -- Acknowledgment -- References -- Chapter 6 Modeling Materials Quantum Properties with Machine Learning -- 6.1 Introduction -- 6.2 Kernel Ridge Regression. , 6.3 Model Assessment -- 6.3.1 Learning Curve -- 6.3.2 Speedup -- 6.4 Representations -- 6.5 Recent Developments -- References -- Chapter 7 Automated Computation of Materials Properties -- 7.1 Introduction -- 7.2 Automated Computational Materials Design Frameworks -- 7.2.1 Generating and Using Databases for Materials Discovery -- 7.2.2 Standardized Protocols for Automated Data Generation -- 7.3 Integrated Calculation of Materials Properties -- 7.3.1 Autonomous Symmetry Analysis -- 7.3.2 Elastic Constants -- 7.3.3 Quasi‐harmonic Debye-Grüneisen Model -- 7.3.4 Harmonic Phonons -- 7.3.5 Quasi‐harmonic Phonons -- 7.3.6 Anharmonic Phonons -- 7.4 Online Data Repositories -- 7.4.1 Computational Materials Data Web Portals -- 7.4.2 Programmatically Accessible Online Repositories of Computed Materials Properties -- 7.5 Materials Applications -- 7.5.1 Disordered Materials -- 7.5.1.1 High Entropy Materials -- 7.5.1.2 Metallic Glasses -- 7.5.1.3 Modeling Off‐Stoichiometry Materials -- 7.5.2 Superalloys -- 7.5.3 Thermoelectrics -- 7.5.4 Magnetic Materials -- 7.6 Conclusion -- Acknowledgments -- References -- Chapter 8 Cognitive Chemistry: The Marriage of Machine Learning and Chemistry to Accelerate Materials Discovery -- 8.1 Introduction -- 8.2 Describing Molecules for Machine Learning Algorithms -- 8.3 Building Fast and Accurate Models with Machine Learning -- 8.3.1 Squared Exponential Kernel -- 8.3.2 Rational Quadratic Kernel -- 8.4 Searching Through Chemical Libraries -- 8.5 Conclusion -- References -- Chapter 9 Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation -- 9.1 Introduction -- 9.2 Machine Learning Potential for Global Optimization -- 9.2.1 Lattice Sums Method -- 9.2.2 Feature Vector -- 9.2.3 Feature Vector Analysis -- 9.2.4 Examples of Machine Learning Interatomic Potentials -- 9.2.4.1 Aluminum. , 9.2.4.2 Carbon -- 9.2.4.3 Helium and Xenon -- 9.2.5 Discussion -- 9.3 Interatomic Potential for Molecular Dynamics -- 9.3.1 General Form of the Potential -- 9.3.2 Parameters Selection -- 9.3.3 Thermodynamic Quantities and Phase Transitions -- 9.3.4 Interatomic Potential for System of Two (or More) Atomic Types -- 9.4 Statistical Approach for Constructing ML Potentials -- 9.4.1 Two‐Body Potential -- 9.4.2 Three‐Body Potential -- Acknowledgements -- References -- Index -- EULA.