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Atomic Structure Prediction of Nanostructures, Clusters and Surfaces.
Title:
Atomic Structure Prediction of Nanostructures, Clusters and Surfaces.
Author:
Ciobanu, Cristian V.
ISBN:
9783527655052
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (207 pages)
Contents:
Atomic Structure Prediction of Nanostructures, Clusters and Surfaces -- Contents -- Preface -- 1 The Challenge of Predicting Atomic Structure -- 1.1 Evolution: Reality and Algorithms -- 1.2 Brief Historical Perspective -- 1.3 Scope and Organization of This Book -- References -- 2 The Genetic Algorithm in Real-Space Representation -- 2.1 Structure Determination Problems -- 2.1.1 Cluster Structure -- 2.1.2 Crystal Structure Prediction -- 2.1.3 Surface Reconstructions -- 2.1.4 Range of Applications -- 2.2 General Procedure -- 2.3 Selection of Parent Structures -- 2.4 Crossover Operations -- 2.4.1 Cut-and-Splice Crossover in Real Space -- 2.4.2 Crossovers and Periodic Boundary Conditions -- 2.5 Mutations -- 2.5.1 Zero-Penalty Mutations -- 2.5.2 Regular Mutations -- 2.6 Updating the Genetic Pool: Survival of the Fittest -- 2.7 Stopping Criteria and Subsequent Analysis -- References -- 3 Crystal Structure Prediction -- 3.1 Complexity of the Energy Landscape -- 3.2 Improving the Efficiency of GA -- 3.3 Interaction Models -- 3.3.1 Classical Potentials -- 3.3.2 Ab Initio Methods -- 3.3.3 Adaptive Classical Potentials -- 3.4 Creating the Generation-Zero Structures -- 3.5 Assessing Structural Diversity of the Pool -- 3.5.1 Fingerprint Functions -- 3.5.2 General Features of the PES -- 3.6 Variable Composition -- 3.7 Examples -- 3.7.1 Identification of Post-Pyrite Phase Transitions -- 3.7.1.1 Computational Details -- 3.7.1.2 Results and Discussion -- 3.7.2 Ultrahigh-Pressure Phases of Ice -- 3.7.2.1 Computational Details -- 3.7.2.2 Results and Discussion -- 3.7.3 Structure and Magnetic Properties of Fe-Co Alloys -- 3.7.3.1 Computational Details -- 3.7.3.2 Results and Discussion -- References -- 4 Optimization of Atomic Clusters -- 4.1 Alloys, Oxides, and Other Cluster Materials -- 4.2 Optimization of Substrate-Supported Clusters via GA.

4.3 GA Solution to the Thomson Problem -- References -- 5 Atomic Structure of Surfaces, Interfaces, and Nanowires -- 5.1 Reconstruction of Semiconductor Surfaces as a Problem of Global Optimization -- 5.1.1 The Genetic Algorithm for Surface Reconstructions: the Case of Si(105) -- 5.1.1.1 Computational Details for Si(105) -- 5.1.1.2 Results for Si(105) -- 5.1.2 New Reconstructions for a Related Surface, Si(103) -- 5.1.3 Model Reconstructions for Si(337), an Unstable Surface: GA Followed by DFT Relaxations -- 5.1.3.1 Results for Si(337) Models -- 5.1.3.2 Discussion -- 5.1.4 Atomic Structure of Steps on High-Index Surfaces -- 5.1.4.1 Supercell Geometry and Algorithm Details -- 5.1.4.2 Results for Step Structures on Si(114) -- 5.2 Genetic Algorithm for Interface Structures -- 5.2.1 GA for Grain Boundary Structure Optimization -- 5.2.2 Structures Generated by GA -- 5.2.3 Grain Boundary Energy Calculations -- 5.3 Nanowire and Nanotube Structures via GA Optimization -- 5.3.1 Passivated Silicon Nanowires -- 5.3.2 One-Dimensional Nanostructures under Radial Confinement -- 5.3.2.1 Introduction -- 5.3.2.2 Description of the Algorithm -- 5.3.2.3 Results for Prototype Nanotubes -- 5.3.2.4 Discussion -- 5.3.2.5 Concluding Remarks -- References -- 6 Other Methodologies for Investigating Atomic Structure -- 6.1 Parallel Tempering Monte Carlo Annealing -- 6.1.1 General Considerations -- 6.1.2 Advantages of the Parallel Tempering Algorithm as a Global Optimizer -- 6.1.3 Description of the Algorithm -- 6.2 Basin Hopping Monte Carlo -- 6.3 Optimization via Minima Hopping -- 6.4 The Metadynamics Approach -- 6.5 Comparative Studies between GA and Other Structural Optimization Techniques -- 6.5.1 Reconstructions of Si(114): Comparison between GA and PTMC -- 6.5.1.1 PTMC Results -- 6.5.1.2 GA Results -- 6.5.1.3 DFT Calculations -- 6.5.1.4 Structural Models for Si(114).

6.5.1.5 Discussion -- 6.5.1.6 Concluding Remarks -- 6.5.2 Crystal Structure Prediction: Comparison between GA and MH -- 6.5.2.1 GA Applied to AlxSc1-x Alloys -- 6.5.2.2 Boron -- 6.5.2.3 Minima Hopping -- References -- 7 Perspectives and Outlook -- 7.1 Expansion through the Community -- 7.2 Future Algorithm Developments -- 7.3 Problems to Tackle - Discovery versus Design -- Index.
Abstract:
This work fills the gap for a comprehensive reference conveying the developments in global optimization of atomic structures using genetic algorithms. Over the last few decades, such algorithms based on mimicking the processes of natural evolution have made their way from computer science disciplines to solid states physics and chemistry, where they have demonstrated their versatility and predictive power for many materials. Following an introduction and historical perspective, the text moves on to provide an in-depth description of the algorithm before describing its applications to crystal structure prediction, atomic clusters, surface and interface reconstructions, and quasi one-dimensional nanostructures. The final chapters provide a brief account of other methods for atomic structure optimization and perspectives on the future of the field.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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