• chemical ordering
• Global Optimization
• Grouping
• nanoalloys
• PES

Structure and chemical ordering are two fundamental pre-requisites to exploit the many and fascinating properties of metallic nanoalloys and computational modeling can provide important information in this respect.
Here, will be shown how the use of smart descriptors can be interfaced with a Grouping scheme, i.e., a form of Coarse-Grained approach applied to metallic nanoalloys, able to simplify the PES of these complex systems (keeping, at the same time, full atomistic resolution) and boosting the efficiency of GO schemes in devising global minima. In this perspective, Grouping demonstrates as a multi-scale approach able to provide a powerful tool for a fast and efficient screening of the different possible chemical ordering patterns that can be realized in a binary alloy by identifying the thermodynamic preferred one.
In this thesis the application of a new developed code realizing a Grouping scheme, after an extensive structural research made by using a Basin Hopping algorithm, will be presented to two prototypical nanoalloys: (i) PdPt, characterized by a tendency towards alloying at the nanoscale; (ii) AgCu, an alloy which already at the nanoscale presents a tendency towards segregation.