Cover
1 Crystallography Open Database: History, Development, and Perspectives
1. 1.1 Introduction
2. 1.2 Open Databases for Science
3. 1.3 Building COD
4. 1.4 Use of COD
5. 1.5 Applications
6. 1.6 Perspectives
7. Acknowledgments
8. References
2 The Inorganic Crystal Structure Database (ICSD): A Tool for Materials Sciences
1. 2.1 Introduction
2. 2.2 Content of ICSD
3. 2.3 Interfaces
4. 2.4 Applications of ICSD
5. 2.5 Outlook
6. References
3 Pauling File: Toward a Holistic View
1. 3.1 Introduction
2. 3.2 PAULING FILE: Crystal Structures
3. 3.3 PAULING FILE: Phase Diagrams
4. 3.4 PAULING FILE: Physical Properties
5. 3.5 Data Quality
6. 3.6 Distinct Phases
7. 3.7 Toward a Megadatabase
8. 3.8 Applications
9. 3.9 Lessons to Learn from Experience
10. 3.10 Conclusion
11. References
4 From Topological Descriptors to Expert Systems: A Route to Predictable Materials
1. 4.1 Introduction
2. 4.2 Topological Tools for Developing Knowledge Databases
3. 4.3 Applications of Topological Tools in Crystal Chemistry and Materials Science
4. 4.4 Conclusions
5. References
5 A High‐Throughput Computational Study Driven by the AiiDA Materials Informatics Framework and the PAULING FILE as Reference Database
1. 5.1 Introduction
2. 5.2 Nature Defines Cornerstones Providing a Marvelously Rich but Still Very Rigid Systematic Framework of Restraint Conditions
3. 5.3 The First, Second, and Third Paradigms
4. 5.4 The Realization of the Fourth and Fifth Paradigms Requires Three Preconditions
5. 5.5 The Core Idea of the Fifth Paradigm
6. 5.6 Restraint Conditions Revealed by “Inorganic Solids Overview–Governing Factor Spaces (Maps)” Discovered by Data‐Mining Techniques
7. 5.7 Quantum Simulation Strategy
8. 5.8 Workflows Engine in AiiDA to Carry Out High‐Throughput Calculation for the Creation of the Materials Cloud, Binaries Edition
9. 5.9 Conclusions
10. Acknowledgment
11. References
6 Modeling Materials Quantum Properties with Machine Learning
1. 6.1 Introduction
2. 6.2 Kernel Ridge Regression
3. 6.3 Model Assessment
4. 6.4 Representations
5. 6.5 Recent Developments
6. References
7 Automated Computation of Materials Properties
1. 7.1 Introduction
2. 7.2 Automated Computational Materials Design Frameworks
3. 7.3 Integrated Calculation of Materials Properties
4. 7.4 Online Data Repositories
5. 7.5 Materials Applications
6. 7.6 Conclusion
7. Acknowledgments
8. References
8 Cognitive Chemistry: The Marriage of Machine Learning and Chemistry to Accelerate Materials Discovery
1. 8.1 Introduction
2. 8.2 Describing Molecules for Machine Learning Algorithms
3. 8.3 Building Fast and Accurate Models with Machine Learning
4. 8.4 Searching Through Chemical Libraries
5. 8.5 Conclusion
6. References
9 Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation
1. 9.1 Introduction
2. 9.2 Machine Learning Potential for Global Optimization
3. 9.3 Interatomic Potential for Molecular Dynamics
4. 9.4 Statistical Approach for Constructing ML Potentials
5. Acknowledgements
6. References
Index
End User License Agreement

List of Tables

Chapter 1
1. Table 1.1 Material property and structure databases available online.
2. Table 1.2 Error classes routinely addressed by the COD maintainers.
Chapter 3
1. Table 3.1 Example of data stored for a PAULING FILE crystal structure e...
2. Table 3.2 The 50 most frequently occurring atomic environment types (AE...
3. Table 3.3 Electronic and electrical and ferroelectric properties consid...
4. Table 3.4 Distinctphases in the Al–Ta system.
5. Table 3.5 Numbers of distinct chemical systems,phases, and entries in P...
6. Table 3.6 Number of entries in PCD‐2016/2017 according to the level of ...
7. Table 3.7 Number of distinctphases in PCD‐2016/2017 comparing the numbe...
Chapter 4
1. Table 4.1 Structural topological descriptors.
2. Table 4.2 Abundant topological types in standard representation and the...
3. Table 4.3 All possible correlations between the ligand coordination mod...
4. Table 4.4 Relationships CML–UNSR–CF for 4196 two‐ or three‐periodic cop...
Chapter 9
1. Table 9.1 Parameters for 3‐body terms used in all the examples in the current se...
2. Table 9.2 Results of our scheme for aluminum:r _low and RMSE_low denote the Pears...
3. Table 9.3 Results of our scheme for carbon:r _low and RMSE_low denote to the Pear...
4. Table 9.4 Experimental and calculated entropies of crystalline Al at different t...

List of Illustrations

Chapter 1
1. Figure 1.1 COD record number growth.
2. Figure 1.2 TCOD record number growth.
3. Figure 1.3 COD search Web interface form.
4. Figure 1.4 COD search result page, obtained as of 05 November 2016 from th...
5. Figure 1.5 Example of the COD programmatic search interface.
6. Figure 1.6 Retrieving a specific COD structure using the stable COD URI id...
7. Figure 1.7 Querying the COD MySQL database.
8. Figure 1.8 Finding column definitions of the COD “data” view.
9. Figure 1.9 Filtering out structures from the COD MySQL queries.
10. Figure 1.10 A COD CIF data retrieval after a MySQL query using COD URIs. T...
11. Figure 1.11 Preparing coordinates for an SQL query using a locally install...
12. Figure 1.12 Obtaining (checking‐out) a working copy of the COD data using ...
13. Figure 1.13 Cloning COD data directory with GIT and GIT SVN.
14. Figure 1.14 Using the “rsync” program to download and update the COD file ...
15. Figure 1.15 Checking out the COD MySQL dumps from the Subversion repositor...
16. Figure 1.16 Extracting the COD MySQL dumps from a ZIP archive of quarterly...
17. Figure 1.17 Search COD CIFs using “grep.” Options of this command are supp...
18. Figure 1.18 Use of “find,” “xargs” and “cifvalues” from the “cod‐tools” pa...
Chapter 2
1. Figure 2.1 Overview of ICSD content by composition of the compounds.
2. Figure 2.2 Visualization using JSMol in ICSD.
3. Figure 2.3 The synoptic view helps to reveal structural similarity.
4. Figure 2.4 Growth of the number of records in ICSD since 1980.
5. Figure 2.5 Basic search screen of ICSD Desktop/Web interface.
6. Figure 2.6 Simulated powder patterns of Al₄H₂(SO₄)₇(H₂O)₂₄ (a) and Cr₄H₂(S...
Chapter 3
1. Figure 3.1 Data sets for RbO and CsS, as published and after standardizati...
2. Figure 3.2 The structure of WAl₅, reported in space group P6₃ (173), can b...
3. Figure 3.3 Next‐neighbor histogram (NNH) (top left) and the corresp...
4. Figure 3.4 Examples of cell parameter plots from Pearson's Crystal Data [2...
5. Figure 3.5 Examples of phase diagrams as redrawn for the PAULING FILE: (a)...
6. Figure 3.6 Part of the data sheet of a physical properties entry for Li₂O ...
7. Figure 3.7 Distribution of the database entries in the PAULING FILE (June ...
8. Figure 3.8 Distribution of the database entries in the PAULING FILE (June ...
9. Figure 3.9 Distribution of database entries in the PAULING FILE (June 2016...
10. Figure 3.10 Number of items in the physical properties part of the PAULING...
11. Figure 3.11 Example of the Constitution Browser in the PAULING FILE – Bina...
12. Figure 3.12 A generalized atomic environment type (AET) matrix PN_A vs. PN_B
13. Figure 3.13 Number of phases vs. the number of atoms per unit cell, consid...
14. Figure 3.14 Number of phases according to the number of different AETs (ri...
15. Figure 3.15 Number of phases according to the crystal system and space gro...
16. Figure 3.16 Total number of point sets observed for the 18 most frequently...
17. Figure 3.17 Number of representatives (phases) of the 100 most common prot...
18. Figure 3.18 Outline of the VEMD project. (a) General overview, (b) databas...
Chapter 4
1. Figure 4.1 The crystal structure of one of the Samson's “monsters,” NaCd₂....
2. Figure 4.2 Steps of the underlying net construction and classification in ...
3. Figure 4.3 Steps of the underlying net construction and classification in ...
4. Figure 4.4 Distribution of 722 012 metal coordination centers.
5. Figure 4.5 Distribution of coordination numbers of 73 530 copper atoms in ...
6. Figure 4.6 Distribution of chemical composition of the coordination polyhe...
7. Figure 4.7 Distribution of distances between copper atoms and nonmetal ato...
8. Figure 4.8 Distribution of the most abundant ligands in the copper coordin...
9. Figure 4.9 Distribution of ligands over most widespread coordination modes...
10. Figure 4.10 Distributions of copper‐containing coordination groups over th...
11. Figure 4.11 Distribution of 3D coordination networks over degree of interp...
12. Figure 4.12 Declarative pattern.
13. Figure 4.13 Advanced declarative pattern.
14. Figure 4.14 Contextual pattern: (a) conceptual diagram and (b) ER model.
15. Figure 4.15 Typed contextual pattern.
16. Figure 4.16 Advanced contextual pattern: conceptual diagram.
17. Figure 4.17 Advanced contextual pattern: (a) the objects part and (b) exam...
18. Figure 4.18 Advanced contextual pattern: (a) the attributes part and (b) e...
19. Figure 4.19 Advanced contextual pattern: (a) the object attributes part an...
20. Figure 4.20 Advanced contextual pattern: (a) the relationships part and (b...
21. Figure 4.21 Advanced contextual pattern: (a) the relationship attributes p...
22. Figure 4.22 Advanced contextual pattern: (a) the object attribute values p...
Chapter 5
1. Figure 5.1 Generalized atomic environment type (AET) (coordination polyhed...
2. Figure 5.2 Outline of the fourth paradigm showing in addition the three ma...
3. Figure 5.3 Outline of the fifth paradigm showing in addition the three maj...
4. Figure 5.4 Separation of 2330 binary systems into compound formers (blue) ...
5. Figure 5.5 Atomic environment type (AET) stability map showing the periodi...
6. Figure 5.6 Occurrences of daltonide inorganic solids vs. available concent...
7. Figure 5.7 An example of a portion of a provenance graph generated by AiiD...
8. Figure 5.8 A simple workflow based on workfunctions showing the definition...
9. Figure 5.9 A toy example of a workchain showing the workflow from Figure 5...
10. Figure 5.10 A schematic of the structure relaxation workflow used througho...
Chapter 6
1. Figure 6.1 Flowchart depicting the training and prediction of properties
2. Figure 6.2 Schematic representation of the differences between a tradition...
3. Figure 6.3 Schematic learning curves for training and test set, depicting ...
4. Figure 6.4 Schematic representation of the learning curve of a good and ba...
5. Figure 6.5 Two homometric molecules (a) and (b), possessing same internal ...
Chapter 7
1. Figure 7.1 Computational materials data generation workflow. (a) Crystallograp...
2. Figure 7.2 Convex hull phase diagrams for multicomponent alloys systems. (a) S...
3. Figure 7.3 Standardized paths in reciprocal space for calculation of the elect...
4. Figure 7.4 Challenges in autonomous symmetry analysis. (a) An illustration of ...
5. Figure 7.5 (a) AEL applies a set of independent normal and shear strains to th...
6. Figure 7.6 (a) Front page of the AFLOW online data repository, highlighting th...
7. Figure 7.7 (a) The AFLOW database is organized as a multilayered system. (b) E...
Chapter 8
1. Figure 8.1 An example of the process of MACCS encoding the molecule diazepam. ...
2. Figure 8.2 A description of the generation of circular (extended connectivity)...
3. Figure 8.3 A graphical representation of the flattening of 3D Cartesian space ...
4. Figure 8.4 A demonstration of the concept of the locality of neurons in a conv...
5. Figure 8.5 (a) Graphical depiction of the construction of a circular fingerpri...
6. Figure 8.6 An example of how sequential information is stored in a recurrent n...
7. Figure 8.7 An example of the generation of an ensemble of directed acyclic gra...
8. Figure 8.8 The deep fingerprints based upon the recurrent neural network metho...
9. Figure 8.9 The accuracy (area under the receiver operating characteristic curv...
10. Figure 8.10 The advantages of pursuing a Δ‐ML method (red) over the equivalent...
11. Figure 8.11 (a) While the properties calculated directly through quantum mecha...
12. Figure 8.12 A graphical representation of the Thompson sampling algorithm. 1 →...
13. Figure 8.13 Information landscapes for the photovoltaic data set (a, a maximiz...
Chapter 9
1. Figure 9.1 Energy landscape. (a) 1D scheme showing the full landscape (sol...
2. Figure 9.2 Energy vs. structural quasi‐entropy correlation. (a) MgO (32 at...
3. Figure 9.3 Reconstructed and real potentials in A_xB_y Lennard‐Jones sys...
4. Figure 9.4 Two quasi‐1D structures ((b) and (c)) that have the same 2‐body...
5. Figure 9.5 Reconstructed pair potential of Al–Al interaction (β = 1)....
6. Figure 9.6 Al–Al interaction potentials at different densities.
7. Figure 9.7 Deletion of features in Al.
8. Figure 9.8 RMSE(E) curves for our models of carbon with different β (a) an...
9. Figure 9.9 Comparison of performances of our best mixed model (a) with kno...
10. Figure 9.10 Deletion of features in C.
11. Figure 9.11 Reconstructed He–He (a) and Xe–Xe (b) pair potentials.
12. Figure 9.12 Deletion of features in He (a) and Xe (b).
13. Figure 9.13 Optimal choice of number of parameters pairs and training set ...
14. Figure 9.14 Comparison of different potentials for Al at 300 K (a) and 200...
15. Figure 9.15 Comparison of different potentials for α‐U at 1000 K (a) and l...
16. Figure 9.16 Phonon density of states at 300 K (a) and 775 K (b): 1 – exper...
17. Figure 9.17 Entropy as a function of temperature: 1 – thermodynamic data f...
18. Figure 9.18 Radial distribution function and melting temperature of Al. (a...
19. Figure 9.19 Determination of the optimal (r _cut, p) set. (a) The dependenc...
20. Figure 9.20 Learning curves for Ti₄H₇ on (a) energies and (b) forces.
21. Figure 9.21 Comparison of ab initio (x) and model (y) projections of force...

Copyright

Editors

Prof. Olexandr Isayev

University of North Carolina at Chapel Hill

UNC Eshelman School of Pharmacy

Campus Box 7568

Chapel Hill, NC

United States

Prof. Alexander Tropsha

University of North Carolina at Chapel Hill

UNC Eshelman School of Pharmacy

Campus Box 7568

Chapel Hill, NC

United States

Prof. Stefano Curtarolo

Duke University

Mechanical Engineering & Mat. Science

144 Hudson Hall

Durham, NC

United States

Cover Image: © Floriana/Getty Images

All books published by Wiley–VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

Library of Congress Card No.:

applied for

British Library Cataloguing–in–Publication Data

A catalogue record for this book is available from the British Library.

Bibliographic information published by the Deutsche Nationalbibliothek

The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at <http://dnb.d–nb.de>.

All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form - by photoprinting, microfilm, or any other means - nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.

Print ISBN: 978–3–527–34121–4

ePDF ISBN: 978–3–527–80225–8

ePub ISBN: 978–3–527–80227–2

oBook ISBN: 978–3–527–80226–5

Methods, Tools and Applications