Details

Materials Informatics


Materials Informatics

Methods, Tools, and Applications
1. Aufl.

von: Olexandr Isayev, Alexander Tropsha, Stefano Curtarolo

97,99 €

Verlag: Wiley-VCH
Format: EPUB
Veröffentl.: 14.08.2019
ISBN/EAN: 9783527802272
Sprache: englisch
Anzahl Seiten: 304

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Beschreibungen

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

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