Atomic Structure Prediction of Nanostructures, Clusters and by Dr. Cristian V. Ciobanu, Dr. Cai?Zhuang Wang, Dr. Kai?Ming

By Dr. Cristian V. Ciobanu, Dr. Cai?Zhuang Wang, Dr. Kai?Ming Ho(auth.)

This paintings fills the distance for a accomplished reference conveying the advancements in worldwide optimization of atomic buildings utilizing genetic algorithms. during the last few many years, such algorithms in accordance with mimicking the approaches of usual evolution have made their method from machine technological know-how disciplines to strong states physics and chemistry, the place they've got confirmed their versatility and predictive energy for lots of fabrics. Following an advent and old viewpoint, the textual content strikes directly to supply an in-depth description of the set of rules ahead of describing its functions to crystal constitution prediction, atomic clusters, floor and interface reconstructions, and quasi one-dimensional nanostructures. the ultimate chapters supply a short account of alternative tools for atomic constitution optimization and views at the way forward for the sector.

Content:
Chapter 1 The problem of Predicting Atomic constitution (pages 1–9):
Chapter 2 The Genetic set of rules in Real?Space illustration (pages 11–35):
Chapter three Crystal constitution Prediction (pages 37–69):
Chapter four Optimization of Atomic Clusters (pages 71–86):
Chapter five Atomic constitution of Surfaces, Interfaces, and Nanowires (pages 87–147):
Chapter 6 different Methodologies for Investigating Atomic constitution (pages 149–186):
Chapter 7 views and Outlook (pages 187–189):

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Extra info for Atomic Structure Prediction of Nanostructures, Clusters and Surfaces

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Elemental silicon) such empirical models are sufficiently good (Stillinger–Weber [16], Tersoff [17], Lenosky et al. ). In these cases, it makes sense for the GA optimization to be carried out using the empirical potential. After the GA run has been carried out with an empirical potential, however, we should use more accurate description of the interactions (such as DFT or tight binding) to recalculate the cost functions of the structure obtained. If the rank ordering of the more energetically favorable structures in the pool does not change too significantly upon recalculation at the level of DFT, then this means that the potential was sufficiently reliable to start with; therefore, after the DFTreordering of the structures, we can have a high degree of confidence in the refined results.

3), or other energetic quantity that is physically motivated by the problem at hand. For a global minimum (as opposed to global maximum) search, it appears more natural to deal with minimizing an energetic quantity (cost function, such as f or c) than to deal with maximizing a fitness function. However, this is purely a matter of choice because the fitness function in this case could simply be the negative of the cost function; for example, we may define as a fitness function the binding energy per atom (in the case of clusters and crystals), which would simply be the negative of the formation energy per atom f defined in previous sections.

1). In this case, relaxation of all child structures considered should be done simultaneously, in a parallel manner. We caution that even if the parent structures have the same number of atoms and composition, the child structure does not necessarily end up with this same number of atoms or composition. One can choose to enforce constant number of atoms or to allow this number to vary within a reasonable range. So far, the latter option has been seen to result in somewhat faster convergence for finding surface reconstructions [15] and for 3D crystal structure predictions [19].

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