Emergence of Bulk CsCl Structure in (CsCl)nCs+ Cluster Ions

The emergence of CsCl bulk structure in (CsCl)nCs+ cluster ions is investigated using a mixed quantum-mechanical/semiempirical theoretical approach. We find that rhombic dodecahedral fragments (with bulk CsCl symmetry) are more stable than rock-salt fragments after the completion of the fifth rhombic dodecahedral atomic shell. From this size (n=184) on, a new set of magic numbers should appear in the experimental mass spectra. We also propose another experimental test for this transition, which explicitely involves the electronic structure of the cluster. Finally, we perform more detailed calculations in the size range n=31--33, where recent experimental investigations have found indications of the presence of rhombic dodecahedral (CsCl)32Cs+ isomers in the cluster beams.Comment: LaTeX file. 6 pages and 4 pictures. Accepted for publication in Phys. Rev.

Study of the Ground-State Geometry of Silicon Clusters Using Artificial Neural Networks

Theoretical determination of the ground-state geometry of Si clusters is a difficult task. As the number of local minima grows exponentially with the number of atoms, to find the global minimum is a real challenge. One may start the search procedure from a random distribution of atoms but it is probably wiser to make use of any available information to restrict the search space. Here, we introduce a new approach, the Assisted Genetic Optimization (AGO) that couples an Artificial Neural Network (ANN) to a Genetic Algorithm (GA). Using available information on small Silicon clusters, we trained an ANN to predict good starting points (initial population) for the GA. AGO is applied to Si10 and Si20 and compared to pure GA. Our results indicate: i) AGO is, at least, 5 times faster than pure GA in our test case; ii) ANN training can be made very fast and successfully plays the role of an experienced investigator; iii) AGO can easily be adapted to other optimization problems