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.

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.

Preface1. The Challenge of Predicting Atomic Structure of Crystals or Nanostructures1.1. Evolution: reality and algorithms1.2. Genetic algorithms and some of their applications1.3. Binary representation1.4. Real-space representation1.5. Organization of this bookReferences for Ch. 12. The Genetic Algorithm in Real-Space Representation2.1. Structure determination problems2.2. General procedure2.3. Selection of parent structures2.4. Crossover operations2.5. Mutations2.6. Updating the genetic pool: Survival of the fittest2.7. Stopping criteria and subsequent analysisReferences for Ch. 23. Crystal Structure Prediction3.1. Complexity of the energy landscape3.2. Interaction models3.2.1. Classical potentials3.2.2. DFT methods3.2.3. Adaptive classical potentials3.3. Constraints for improving the efficiency of GA3.4. Assessing the diversity of the pool3.4.1. Fingerprint function3.4.2. Maintaining the diversity of the pool3.5. GA for variable-composition3.6. Mapping out phase diagrams3.7. ExamplesReferences for Ch. 34. Optimization of Atomic Clusters4.1. Lennard-Jones clusters4.2. Thompson problem for charged systems4.3. Metal clustersReferences for Ch. 45. Atomic Structure of Surfaces, Interfaces, and Nanowires5.1. Reconstruction of surfaces as problem of global optimization5.2. Interface structure: tilted grain boundaries in Silicon5.3. Nanowires and nanotubes via GA optimizationReferences for Ch. 56. Other Methodologies for Atomic Structure Studies6.1. Parallel-tempering Monte Carlo with geometric cooling schedule6.2. Basin-hoping Monte Carlo6.3. Minima-hoping method6.4. Metadynamics approach for predicting phase transformationsReferences for Ch. 67. Perspectives and Future DirectionsReferences for Ch. 7