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E-raamat: Computational Materials Discovery

Edited by (Skolkovo Institute of Science and Technology, Russia), Edited by (Moscow Institute of Physics and Technology, Russia), Edited by (Skolkovo Institute of Science and Technology, Russia)
  • Formaat: 456 pages
  • Ilmumisaeg: 30-Oct-2018
  • Kirjastus: Royal Society of Chemistry
  • Keel: eng
  • ISBN-13: 9781788010122
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Computational Materials DiscoverySuurem pilt
  • Formaat: 456 pages
  • Ilmumisaeg: 30-Oct-2018
  • Kirjastus: Royal Society of Chemistry
  • Keel: eng
  • ISBN-13: 9781788010122
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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.
Chapter 1 Computational Materials Discovery: Dream or Reality? 1(14)
Artem R. Oganov
Alexander G. Kvashnin
Gabriele Saleh
Acknowledgements
10(1)
References
10(5)
Chapter 2 Computational Materials Discovery Using Evolutionary Algorithms 15(51)
Artem R. Oganov
Ivan Kruglov
Jin Zhang
M. Mahdi Davari Esfahani
2.1 A Bit of Theory
16(3)
2.1.1 Combinatorial Complexity of the Problem
16(3)
2.2 How the Method Works
19(15)
2.2.1 Initialization
21(1)
2.2.2 Representation
21(3)
2.2.3 Fitness Function
24(3)
2.2.4 Selection
27(1)
2.2.5 Variation Operators
27(1)
2.2.6 How to Avoid Getting Stuck to Local Minima
28(1)
2.2.7 Extension to Variable-composition Systems
29(1)
2.2.8 Extension to Molecular Crystals
30(2)
2.2.9 A Few Comments on the Performance of the Method
32(2)
2.3 A Few Illustrations of the Method
34(25)
2.3.1 Novel Chemistry of the Elements Under Pressure
34(6)
2.3.2 Low-dimensional States of the Elements
40(1)
2.3.3 Discovering New Chemical Compounds at High Pressure... and Even at Zero Pressure
41(7)
2.3.4 Hunt for High-Tc Superconductivity
48(4)
2.3.5 Low-dimensional Systems: Surfaces, Polymers, Nanoparticles, Proteins
52(7)
2.4 Conclusions
59(1)
Acknowledgements
59(1)
References
59(7)
Chapter 3 Applications of Machine Learning for Representing Interatomic Interactions 66(21)
Alexander V. Shapeev
3.1 Introduction
66(3)
3.1.1 Quantum-mechanical Models
67(1)
3.1.2 Empirical Interatomic Potentials
67(1)
3.1.3 Machine Learning Interatomic Potentials
68(1)
3.2 Simple Problem: Fitting of Potential Energy Surfaces
69(2)
3.2.1 Representation of Atomic Systems
69(1)
3.2.2 An Overview of Machine Learning Methods
70(1)
3.3 Machine Learning Interatomic Potentials
71(6)
3.3.1 Representation of Atomic Environments
73(1)
3.3.2 Existing MLIPs
74(3)
3.4 Fitting and Testing of Interatomic Potentials
77(5)
3.4.1 Optimization Algorithms
77(1)
3.4.2 Validation and Cross-validation
78(1)
3.4.3 Learning on the Fly
79(3)
3.5 Discussion
82(1)
3.5.1 Which Potential Is Better?
82(1)
3.5.2 Open Problems in MLIP Development
82(1)
3.6 Further Reading
83(1)
References
84(3)
Chapter 4 Embedding Methods in Materials Discovery 87(30)
Peter V. Sushko
Chen Huang
Niranjan Govind
Karol Kowalski
4.1 Preamble
87(1)
4.2 Background
88(2)
4.3 Embedding Methods
90(8)
4.3.1 Partitioning of the Structure and Interactions
91(5)
4.3.2 Self-consistent Embedding
96(1)
4.3.3 Beyond DFT Treatment of the Cluster Part-Viva Quantum Chemistry
97(1)
4.4 Applications
98(7)
4.4.1 Why Embedding?
98(1)
4.4.2 Energetics
99(1)
4.4.3 Spectroscopic Properties
100(3)
4.4.4 Electronic Properties
103(1)
4.4.5 Hybrid Embedding Approach
104(1)
4.4.6 Derivation of Model Parameters
105(1)
4.5 Outlook
105(1)
Acknowledgements
106(1)
References
106(11)
Chapter 5 Chemical Bonding Investigations for Materials 117(59)
Gabriele Saleh
Davide Ceresoli
Giovanni Macetti
Carlo Gatti
5.1 Introduction
117(1)
5.2 Paradigms of Chemistry and Chemical Bonding Descriptors
118(36)
5.2.1 Topological Methods, Quantum Chemical Topology and Beyond
118(26)
5.2.2 Orbital Based Methods
144(10)
5.3 Selected Applications
154(12)
5.3.1 Charge Transfer and Bonding in y-Boron
155(5)
5.3.2 Xe Oxides
160(3)
5.3.3 He Forms Compounds at High Pressure
163(2)
5.3.4 Phase Change Materials
165(1)
5.4 Conclusion
166(1)
Acknowledgements
166(1)
References
166(10)
Chapter 6 Computational Design of Photovoltaic Materials 176(22)
Keith T. Butler
Daniel W. Davies
Aron Walsh
6.1 Introduction
176(1)
6.2 The Design Process
177(3)
6.2.1 Requirements
178(1)
6.2.2 Design
178(1)
6.2.3 Development
179(1)
6.2.4 Testing
180(1)
6.3 Practical Computational Techniques
180(1)
6.4 The Scale of the Search
181(4)
6.4.1 The Combinatorial Approach
181(2)
6.4.2 Chemical Filters and Simple Descriptors
183(2)
6.5 New Materials for Photovoltaics
185(9)
6.5.1 Hierarchy of Screening
187(6)
6.5.2 Bespoke Figures of Merit
193(1)
6.6 Conclusions
194(1)
Acknowledgements
194(1)
References
195(3)
Chapter 7 First-Principles Computational Approaches to Superconducting Transition Temperatures: Phonon-Mediated Mechanism and Beyond 198(42)
Ryosuke Akashi
7.1 Introduction
198(1)
7.2 Theory of Phonon-mediated Superconductivity
199(22)
7.2.1 Eliashberg Theory
200(8)
7.2.2 Density Functional Theory for Superconductors
208(12)
7.2.3 Comparison between the ME Theory and SCDFT
220(1)
7.3 First-Principles Calculation
221(4)
7.3.1 The Workflow
221(2)
7.3.2 Integration of Singular Functions
223(2)
7.4 Applications
225(7)
7.4.1 Eliashberg Equations
225(1)
7.4.2 SCDFT Gap Equation
226(4)
7.4.3 A Case Study: Hydrogen Sulfide
230(2)
7.5 Discussions and Concluding Remarks
232(2)
References
234(6)
Chapter 8 Quest for New Thermoelectric Materials 240(53)
Vladan Stevanovic
Prashun Gorai
Brenden Ortiz
Eric S. Toberer
8.1 Introduction
240(2)
8.2 Brief Introduction to Boltzmann Transport Theory of Thermoelectric Phenomena
242(7)
8.2.1 General Concepts
243(1)
8.2.2 Relaxation Time Approximation
244(3)
8.2.3 Thermoelectric Figure of Merit
247(2)
8.3 Search Strategies and Design Metrics
249(11)
8.3.1 Reduced Power Factors σS2/τ and σS2/λ
249(5)
8.3.2 Thermoelectric Quality Factor β
254(3)
8.3.3 Lattice Thermal Conductivity kappaL
257(3)
8.4 Computational Searches
260(16)
8.4.1 Chemical and Structural Search Spaces
260(3)
8.4.2 Examples of High-throughput Searches
263(6)
8.4.3 Examples of Targeted and Data-driven Searches
269(4)
8.4.4 Discoveries from High-throughput Computational Searches
273(3)
8.5 Role of Experimental Validation
276(6)
8.5.1 Experimental Collaborators
276(1)
8.5.2 Validation of Predicted Properties
276(2)
8.5.3 High zT Demonstration
278(4)
8.6 Outstanding Challenges
282(2)
8.6.1 Dopability of Semiconductors
282(1)
8.6.2 Materials at Elevated Temperatures
283(1)
8.6.3 Beyond Boltzmann Transport
283(1)
Acknowledgements
284(1)
References
284(9)
Chapter 9 Rational Design of Polymer Dielectrics: An Application of Density Functional Theory and Machine Learning 293(27)
A. Mannodi-Kanakkithodi
R. Ramprasad
9.1 Introduction
293(6)
9.1.1 General Background
293(2)
9.1.2 Polymers as Capacitor Dielectrics
295(4)
9.2 Organic and Organometallic Polymers as Dielectrics
299(5)
9.2.1 High-throughput DFT on an Organic Polymer Chemical Space
300(1)
9.2.2 Initial Guidance to Experiments
301(1)
9.2.3 Moving Beyond Pure Organics: An Organometallic Polymer Chemical Space
302(2)
9.3 Synthetic Successes
304(3)
9.4 Learning From Computational Data
307(5)
9.4.1 Polymer Fingerprinting
308(1)
9.4.2 ML Models Trained using DFT Data
308(3)
9.4.3 Validation and Utility of ML Framework
311(1)
9.5 Exploring the Polymer Genome
312(1)
9.6 Conclusions and Outlook
313(1)
Acknowledgements
314(1)
References
314(6)
Chapter 10 Rationalising and Predicting the Structure and Bonding of Bare and Ligated Transition Metal Clusters and Nanoparticles 320(32)
Gilles Frapper
Jean-Francois Halet
10.1 Introduction
320(2)
10.2 Theoretical Models
322(15)
10.3 Quantitative Theoretical Approach
337(2)
10.4 Large Ligated Transition Metal Clusters
339(3)
10.5 The Role of Protective Ligands in Ligated Transition Metal Nanoparticles
342(1)
10.6 Bare Nanoparticles
343(2)
10.7 Conclusion
345(1)
Acknowledgements
346(1)
References
346(6)
Chapter 11 Recent Advances in the Theory of Non-carbon Nanotubes 352(40)
Andrey N. Enyashin
11.1 Introduction
352(1)
11.2 Basic Concepts of Design and after Design of Inorganic Nanotubes
353(5)
11.3 General Criteria Describing the Stability of Inorganic Nanotubes
358(4)
11.4 Mechanical Properties of Inorganic Nanotubes
362(7)
11.4.1 Tensile Deformation
363(2)
11.4.2 Twist Deformation
365(1)
11.4.3 Lateral Compression
366(3)
11.5 Electronic Properties of Inorganic Nanotubes
369(7)
11.5.1 Pristine Nanotubes
369(2)
11.5.2 Inorganic Nanotubes with Intrinsic Defects
371(1)
11.5.3 Inorganic Nanotubes with Extrinsic Defects
372(3)
11.5.4 Magnetic Properties of Inorganic Nanotubes
375(1)
11.6 Capillary Properties of Inorganic Nanotubes
376(7)
11.6.1 Thermodynamics Within Core-Shell Nanotubes
377(2)
11.6.2 Kinetics of Capillary Filling by Molten Salts
379(1)
11.6.3 Kinetics of Capillary Filling by Water
380(3)
11.7 Conclusion
383(1)
Acknowledgements
384(1)
References
384(8)
Chapter 12 Discovery of Novel Topological Materials Via High-throughput Computational Search 392(31)
Gabriel Autes
Oleg V. Yazyev
12.1 Introduction
392(3)
12.2 Topological Materials
395(7)
12.2.1 Topological Insulators
395(5)
12.2.2 Topological Semimetals
400(2)
12.3 High-throughput Search Methodology
402(6)
12.3.1 Symmetry and Composition Prescreening
402(1)
12.3.2 Electronic Structure Calculations
403(1)
12.3.3 First-principles Calculations of Topological Invariants
404(3)
12.3.4 Post Processing
407(1)
12.4 Examples of Materials Discovered Using the High-throughput Screening
408(7)
12.4.1 βBi4I4: a Quasi-one-dimensional Z2 Topological Insulator
408(4)
12.4.2 MoP2 and WP2: Robust Type-II Weyl Semimetals
412(3)
12.5 Conclusions and Outlook
415(1)
References
415(8)
Chapter 13 Computational Discovery of Organic LED Materials 423(24)
Rafael Gomez-Bombarelli
Alan Aspuru-Guzik
13.1 Organic Light-Emitting Diodes and Virtual Discovery
424(4)
13.2 Molecular Search Space
428(3)
13.2.1 Library Generation
429(2)
13.2.2 Genetic Algorithms
431(1)
13.3 Target Properties and Computational Methods
431(5)
13.3.1 Molecular Properties
432(3)
13.3.2 Bulk Properties and Bath Interactions
435(1)
13.4 Other Software Tools
436(3)
13.4.1 Artificial Intelligence
436(1)
13.4.2 Collaborative Decision-making
436(3)
13.5 Reported Materials
439(1)
13.6 Conclusions
439(2)
Abbreviations
441(1)
References
441(6)
Subject Index 447
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