Atomic Structure Prediction of Nanostructures, Clusters and Surfaces

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.

Professor Cristian Ciobanu has received his degrees at Ohio State University before working at Brown University and then joining Colorado School of Mines. Other assignments have taken him to the University of Leiden, NL, and to Cornell.
Cai-Zhuan Wang is Senior Physicist with Ames Laboratory and Iowa State University. Kai-Ming Ho is professor at Iowa State, and also works for Ames Laboratory.
Their research focuses on mechanics and physics of nanowires, self-organized nanostructures, theory and computational modeling of crystal surface phenomena, and development of methodologies for optimization of nanostructures.

Preface

1. The Challenge of Predicting Atomic Structure of Crystals or Nanostructures
1.1. Evolution: reality and algorithms
1.2. Genetic algorithms and some of their applications
1.3. Binary representation
1.4. Real-space representation
1.5. Organization of this book
References for Ch. 1

2. The Genetic Algorithm in Real-Space Representation
2.1. Structure determination problems
2.2. General procedure
2.3. Selection of parent structures
2.4. Crossover operations
2.5. Mutations
2.6. Updating the genetic pool: Survival of the fittest
2.7. Stopping criteria and subsequent analysis
References for Ch. 2

3. Crystal Structure Prediction
3.1. Complexity of the energy landscape
3.2. Interaction models
3.2.1. Classical potentials
3.2.2. DFT methods
3.2.3. Adaptive classical potentials
3.3. Constraints for improving the efficiency of GA
3.4. Assessing the diversity of the pool
3.4.1. Fingerprint function
3.4.2. Maintaining the diversity of the pool
3.5. GA for variable-composition
3.6. Mapping out phase diagrams
3.7. Examples
References for Ch. 3

4. Optimization of Atomic Clusters
4.1. Lennard-Jones clusters
4.2. Thompson problem for charged systems
4.3. Metal clusters
References for Ch. 4

5. Atomic Structure of Surfaces, Interfaces, and Nanowires
5.1. Reconstruction of surfaces as problem of global optimization
5.2. Interface structure: tilted grain boundaries in Silicon
5.3. Nanowires and nanotubes via GA optimization
References for Ch. 5

6. Other Methodologies for Atomic Structure Studies
6.1. Parallel-tempering Monte Carlo with geometric cooling schedule
6.2. Basin-hoping Monte Carlo
6.3. Minima-hoping method
6.4. Metadynamics approach for predicting phase transformations
References for Ch. 6

7. Perspectives and Future Directions
References for Ch. 7