BOOK
Computational Materials Discovery
Artem R Oganov | Gabriele Saleh | Alexander G Kvashnin
(2018)
Additional Information
Book Details
Abstract
New technologies are made possible by new materials, and until recently new materials could only be discovered experimentally. Recent advances in solving the crystal structure prediction problem means that the computational design of materials is now a reality.
Computational Materials Discovery provides a comprehensive review of this field covering different computational methodologies as well as specific applications of materials design. The book starts by illustrating how and why first-principle calculations have gained importance in the process of materials discovery. The book is then split into three sections, the first exploring different approaches and ideas including crystal structure prediction from evolutionary approaches, data mining methods and applications of machine learning. Section two then looks at examples of designing specific functional materials with special technological relevance for example photovoltaic materials, superconducting materials, topological insulators and thermoelectric materials. The final section considers recent developments in creating low-dimensional materials.
With contributions from pioneers and leaders in the field, this unique and timely book provides a convenient entry point for graduate students, researchers and industrial scientists on both the methodologies and applications of the computational design of materials.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| Copyright | iv | ||
| Editor Biographies | v | ||
| Contents | vii | ||
| Chapter 1 Computational Materials Discovery: Dream or Reality? | 1 | ||
| Acknowledgements | 10 | ||
| References | 10 | ||
| Chapter 2 Computational Materials Discovery Using Evolutionary Algorithms | 15 | ||
| 2.1 A Bit of Theory | 16 | ||
| 2.1.1 Combinatorial Complexity of the Problem | 16 | ||
| 2.2 How the Method Works | 19 | ||
| 2.2.1 Initialization | 21 | ||
| 2.2.2 Representation | 21 | ||
| 2.2.3 Fitness Function | 24 | ||
| 2.2.4 Selection | 27 | ||
| 2.2.5 Variation Operators | 27 | ||
| 2.2.6 How to Avoid Getting Stuck to Local Minima | 28 | ||
| 2.2.7 Extension to Variable-composition Systems | 29 | ||
| 2.2.8 Extension to Molecular Crystals | 30 | ||
| 2.2.9 A Few Comments on the Performance of the Method | 32 | ||
| 2.3 A Few Illustrations of the Method | 34 | ||
| 2.3.1 Novel Chemistry of the Elements Under Pressure | 34 | ||
| 2.3.2 Low-dimensional States of the Elements | 40 | ||
| 2.3.3 Discovering New Chemical Compounds at High Pressure… and Even at Zero Pressure | 41 | ||
| 2.3.4 Hunt for High-Tc Superconductivity | 48 | ||
| 2.3.5 Low-dimensional Systems: Surfaces, Polymers, Nanoparticles, Proteins | 52 | ||
| 2.4 Conclusions | 59 | ||
| Acknowledgements | 59 | ||
| References | 59 | ||
| Chapter 3 Applications of Machine Learning for Representing Interatomic Interactions | 66 | ||
| 3.1 Introduction | 66 | ||
| 3.1.1 Quantum-mechanical Models | 67 | ||
| 3.1.2 Empirical Interatomic Potentials | 67 | ||
| 3.1.3 Machine Learning Interatomic Potentials | 68 | ||
| 3.2 Simple Problem: Fitting of Potential Energy Surfaces | 69 | ||
| 3.2.1 Representation of Atomic Systems | 69 | ||
| 3.2.2 An Overview of Machine Learning Methods | 70 | ||
| 3.3 Machine Learning Interatomic Potentials | 71 | ||
| 3.3.1 Representation of Atomic Environments | 73 | ||
| 3.3.2 Existing MLIPs | 74 | ||
| 3.4 Fitting and Testing of Interatomic Potentials | 77 | ||
| 3.4.1 Optimization Algorithms | 77 | ||
| 3.4.2 Validation and Cross-validation | 78 | ||
| 3.4.3 Learning on the Fly | 79 | ||
| 3.5 Discussion | 82 | ||
| 3.5.1 Which Potential Is Better? | 82 | ||
| 3.5.2 Open Problems in MLIP Development | 82 | ||
| 3.6 Further Reading | 83 | ||
| References | 84 | ||
| Chapter 4 Embedding Methods in Materials Discovery | 87 | ||
| 4.1 Preamble | 87 | ||
| 4.2 Background | 88 | ||
| 4.3 Embedding Methods | 90 | ||
| 4.3.1 Partitioning of the Structure and Interactions | 91 | ||
| 4.3.2 Self-consistent Embedding | 96 | ||
| 4.3.3 Beyond DFT Treatment of the Cluster Part – Viva Quantum Chemistry | 97 | ||
| 4.4 Applications | 98 | ||
| 4.4.1 Why Embedding? | 98 | ||
| 4.4.2 Energetics | 99 | ||
| 4.4.3 Spectroscopic Properties | 100 | ||
| 4.4.4 Electronic Properties | 103 | ||
| 4.4.5 Hybrid Embedding Approach | 104 | ||
| 4.4.6 Derivation of Model Parameters | 105 | ||
| 4.5 Outlook | 105 | ||
| Acknowledgements | 106 | ||
| References | 106 | ||
| Chapter 5 Chemical Bonding Investigations for Materials | 117 | ||
| 5.1 Introduction | 117 | ||
| 5.2 Paradigms of Chemistry and Chemical Bonding Descriptors | 118 | ||
| 5.2.1 Topological Methods, Quantum Chemical Topology and Beyond | 118 | ||
| 5.2.2 Orbital Based Methods | 144 | ||
| 5.3 Selected Applications | 154 | ||
| 5.3.1 Charge Transfer and Bonding in γ-Boron | 155 | ||
| 5.3.2 Xe Oxides | 160 | ||
| 5.3.3 He Forms Compounds at High Pressure | 163 | ||
| 5.3.4 Phase Change Materials | 165 | ||
| 5.4 Conclusion | 166 | ||
| Acknowledgements | 166 | ||
| References | 166 | ||
| Chapter 6 Computational Design of Photovoltaic Materials | 176 | ||
| 6.1 Introduction | 176 | ||
| 6.2 The Design Process | 177 | ||
| 6.2.1 Requirements | 178 | ||
| 6.2.2 Design | 178 | ||
| 6.2.3 Development | 179 | ||
| 6.2.4 Testing | 180 | ||
| 6.3 Practical Computational Techniques | 180 | ||
| 6.4 The Scale of the Search | 181 | ||
| 6.4.1 The Combinatorial Approach | 181 | ||
| 6.4.2 Chemical Filters and Simple Descriptors | 183 | ||
| 6.5 New Materials for Photovoltaics | 185 | ||
| 6.5.1 Hierarchy of Screening | 187 | ||
| 6.5.2 Bespoke Figures of Merit | 193 | ||
| 6.6 Conclusions | 194 | ||
| Acknowledgements | 194 | ||
| References | 195 | ||
| Chapter 7 First-Principles Computational Approaches to Superconducting Transition Temperatures: Phonon-Mediated Mechanism and Beyond | 198 | ||
| 7.1 Introduction | 198 | ||
| 7.2 Theory of Phonon-mediated Superconductivity | 199 | ||
| 7.2.1 Eliashberg Theory | 200 | ||
| 7.2.2 Density Functional Theory for Superconductors | 208 | ||
| 7.2.3 Comparison between the ME Theory and SCDFT | 220 | ||
| 7.3 First-Principles Calculation | 221 | ||
| 7.3.1 The Workflow | 221 | ||
| 7.3.2 Integration of Singular Functions | 223 | ||
| 7.4 Applications | 225 | ||
| 7.4.1 Eliashberg Equations | 225 | ||
| 7.4.2 SCDFT Gap Equation | 226 | ||
| 7.4.3 A Case Study: Hydrogen Sulfide | 230 | ||
| 7.5 Discussions and Concluding Remarks | 232 | ||
| References | 234 | ||
| Chapter 8 Quest for New Thermoelectric Materials | 240 | ||
| 8.1 Introduction | 240 | ||
| 8.2 Brief Introduction to Boltzmann Transport Theory of Thermoelectric Phenomena | 242 | ||
| 8.2.1 General Concepts | 243 | ||
| 8.2.2 Relaxation Time Approximation | 244 | ||
| 8.2.3 Thermoelectric Figure of Merit | 247 | ||
| 8.3 Search Strategies and Design Metrics | 249 | ||
| 8.3.1 Reduced Power Factors sS2/t and sS2/γ | 249 | ||
| 8.3.2 Thermoelectric Quality Factor β | 254 | ||
| 8.3.3 Lattice Thermal Conductivity kL | 257 | ||
| 8.4 Computational Searches | 260 | ||
| 8.4.1 Chemical and Structural Search Spaces | 260 | ||
| 8.4.2 Examples of High-throughput Searches | 263 | ||
| 8.4.3 Examples of Targeted and Data-driven Searches | 269 | ||
| 8.4.4 Discoveries from High-throughput Computational Searches | 273 | ||
| 8.5 Role of Experimental Validation | 276 | ||
| 8.5.1 Experimental Collaborators | 276 | ||
| 8.5.2 Validation of Predicted Properties | 276 | ||
| 8.5.3 High zT Demonstration | 278 | ||
| 8.6 Outstanding Challenges | 282 | ||
| 8.6.1 Dopability of Semiconductors | 282 | ||
| 8.6.2 Materials at Elevated Temperatures | 283 | ||
| 8.6.3 Beyond Boltzmann Transport | 283 | ||
| Acknowledgements | 284 | ||
| References | 284 | ||
| Chapter 9 Rational Design of Polymer Dielectrics: An Application of Density Functional Theory and Machine Learning | 293 | ||
| 9.1 Introduction | 293 | ||
| 9.1.1 General Background | 293 | ||
| 9.1.2 Polymers as Capacitor Dielectrics | 295 | ||
| 9.2 Organic and Organometallic Polymers as Dielectrics | 299 | ||
| 9.2.1 High-throughput DFT on an Organic Polymer Chemical Space | 300 | ||
| 9.2.2 Initial Guidance to Experiments | 301 | ||
| 9.2.3 Moving Beyond Pure Organics: An Organometallic Polymer Chemical Space | 302 | ||
| 9.3 Synthetic Successes | 304 | ||
| 9.4 Learning From Computational Data | 307 | ||
| 9.4.1 Polymer Fingerprinting | 308 | ||
| 9.4.2 ML Models Trained using DFT Data | 308 | ||
| 9.4.3 Validation and Utility of ML Framework | 311 | ||
| 9.5 Exporing the Polymer Genome | 312 | ||
| 9.6 Conclusions and Outlook | 313 | ||
| Acknowledgements | 314 | ||
| References | 314 | ||
| Chapter 10 Rationalising and Predicting the Structure and Bonding of Bare and Ligated Transition Metal Clusters and Nanoparticles | 320 | ||
| 10.1 Introduction | 320 | ||
| 10.2 Theoretical Models | 322 | ||
| 10.3 Quantitative Theoretical Approach | 337 | ||
| 10.4 Large Ligated Transition Metal Clusters | 339 | ||
| 10.5 The Role of Protective Ligands in Ligated Transition Metal Nanoparticles | 342 | ||
| 10.6 Bare Nanoparticles | 343 | ||
| 10.7 Conclusion | 345 | ||
| Acknowledgements | 346 | ||
| References | 346 | ||
| Chapter 11 Recent Advances in the Theory of Non-carbon Nanotubes | 352 | ||
| 11.1 Introduction | 352 | ||
| 11.2 Basic Concepts of Design and after Design of Inorganic Nanotubes | 353 | ||
| 11.3 General Criteria Describing the Stability of Inorganic Nanotubes | 358 | ||
| 11.4 Mechanical Properties of Inorganic Nanotubes | 362 | ||
| 11.4.1 Tensile Deformation | 363 | ||
| 11.4.2 Twist Deformation | 365 | ||
| 11.4.3 Lateral Compression | 366 | ||
| 11.5 Electronic Properties of Inorganic Nanotubes | 369 | ||
| 11.5.1 Pristine Nanotubes | 369 | ||
| 11.5.2 Inorganic Nanotubes with Intrinsic Defects | 371 | ||
| 11.5.3 Inorganic Nanotubes with Extrinsic Defects | 372 | ||
| 11.5.4 Magnetic Properties of Inorganic Nanotubes | 375 | ||
| 11.6 Capillary Properties of Inorganic Nanotubes | 376 | ||
| 11.6.1 Thermodynamics Within Core–Shell Nanotubes | 377 | ||
| 11.6.2 Kinetics of Capillary Filling by Molten Salts | 379 | ||
| 11.6.3 Kinetics of Capillary Filling by Water | 380 | ||
| 11.7 Conclusion | 383 | ||
| Acknowledgements | 384 | ||
| References | 384 | ||
| Chapter 12 Discovery of Novel Topological Materials Via High-throughput Computational Search | 392 | ||
| 12.1 Introduction | 392 | ||
| 12.2 Topological Materials | 395 | ||
| 12.2.1 Topological Insulators | 395 | ||
| 12.2.2 Topological Semimetals | 400 | ||
| 12.3 High-throughput Search Methodology | 402 | ||
| 12.3.1 Symmetry and Composition Prescreening | 402 | ||
| 12.3.2 Electronic Structure Calculations | 403 | ||
| 12.3.3 First-principles Calculations of Topological Invariants | 404 | ||
| 12.3.4 Post Processing | 407 | ||
| 12.4 Examples of Materials Discovered Using the High-throughput Screening | 408 | ||
| 12.4.1 β-Bi4I4: a Quasi-one-dimensional Z2 Topological Insulator | 408 | ||
| 12.4.2 MoP2 and WP2: Robust Type-II Weyl Semimetals | 412 | ||
| 12.5 Conclusions and Outlook | 415 | ||
| References | 415 | ||
| Chapter 13 Computational Discovery of Organic LED Materials | 423 | ||
| 13.1 Organic Light-Emitting Diodes and Virtual Discovery | 424 | ||
| 13.2 Molecular Search Space | 428 | ||
| 13.2.1 Library Generation | 429 | ||
| 13.2.2 Genetic Algorithms | 431 | ||
| 13.3 Target Properties and Computational Methods | 431 | ||
| 13.3.1 Molecular Properties | 432 | ||
| 13.3.2 Bulk Properties and Bath Interactions | 435 | ||
| 13.4 Other Software Tools | 436 | ||
| 13.4.1 Artificial Intelligence | 436 | ||
| 13.4.2 Collaborative Decision-making | 436 | ||
| 13.5 Reported Materials | 439 | ||
| 13.6 Conclusions | 439 | ||
| Abbreviations | 441 | ||
| References | 441 | ||
| Subject Index | 447 |