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Computational materials discovery /
A unique and timely book providing an overview of both the methodologies and applications of computational materials design.
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge :
Royal Society of Chemistry,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright; Editor Biographies; Contents; Chapter 1 Computational Materials Discovery: Dream or Reality?; Acknowledgements; References; Chapter 2 Computational Materials Discovery Using Evolutionary Algorithms; 2.1 A Bit of Theory; 2.1.1 Combinatorial Complexity of the Problem; 2.2 How the Method Works; 2.2.1 Initialization; 2.2.2 Representation; 2.2.3 Fitness Function; 2.2.4 Selection; 2.2.5 Variation Operators; 2.2.6 How to Avoid Getting Stuck to Local Minima; 2.2.7 Extension to Variable-composition Systems; 2.2.8 Extension to Molecular Crystals
- 2.2.9 A Few Comments on the Performance of the Method2.3 A Few Illustrations of the Method; 2.3.1 Novel Chemistry of the Elements Under Pressure; 2.3.2 Low-dimensional States of the Elements; 2.3.3 Discovering New Chemical Compounds at High Pressure ... and Even at Zero Pressure; 2.3.4 Hunt for High-Tc Superconductivity; 2.3.5 Low-dimensional Systems: Surfaces, Polymers, Nanoparticles, Proteins; 2.4 Conclusions; Acknowledgements; References; Chapter 3 Applications of Machine Learning for Representing Interatomic Interactions; 3.1 Introduction; 3.1.1 Quantum-mechanical Models
- 3.1.2 Empirical Interatomic Potentials3.1.3 Machine Learning Interatomic Potentials; 3.2 Simple Problem: Fitting of Potential Energy Surfaces; 3.2.1 Representation of Atomic Systems; 3.2.2 An Overview of Machine Learning Methods; 3.3 Machine Learning Interatomic Potentials; 3.3.1 Representation of Atomic Environments; 3.3.2 Existing MLIPs; 3.4 Fitting and Testing of Interatomic Potentials; 3.4.1 Optimization Algorithms; 3.4.2 Validation and Cross-validation; 3.4.3 Learning on the Fly; 3.5 Discussion; 3.5.1 Which Potential Is Better?; 3.5.2 Open Problems in MLIP Development
- 3.6 Further ReadingReferences; Chapter 4 Embedding Methods in Materials Discovery; 4.1 Preamble; 4.2 Background; 4.3 Embedding Methods; 4.3.1 Partitioning of the Structure and Interactions; 4.3.2 Self-consistent Embedding; 4.3.3 Beyond DFT Treatment of the Cluster Part
- Viva Quantum Chemistry; 4.4 Applications; 4.4.1 Why Embedding?; 4.4.2 Energetics; 4.4.3 Spectroscopic Properties; 4.4.4 Electronic Properties; 4.4.5 Hybrid Embedding Approach; 4.4.6 Derivation of Model Parameters; 4.5 Outlook; Acknowledgements; References; Chapter 5 Chemical Bonding Investigations for Materials