  We present a universal method for the large-scale prediction of the atomic
structure of clusters. Our algorithm performs the joint evolutionary search for
all clusters in a given area of the compositional space and takes advantage of
structural similarities frequently observed in clusters of close compositions.
The resulting speedup is up to 50 times compared to current methods. This
enables the first-principles studies of multi-component clusters with full
coverage of a wide range of compositions. As an example, we report an
unprecedented first-principles global optimization of 315 SinOm clusters with
n<=15 and m<=20. The obtained map of Si-O cluster stability shows the existence
of both expected (SiO2)n and unexpected (e.g. Si4O18) stable ("magic")
clusters, which can be important for miscellaneous applications.
