Method for Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions

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 first-principles studies of multicomponent clusters with full coverage of a wide range of compositions. As an example, we report an unprecedented first-principles global optimization of 315 Si _nO _m 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 a variety of applications.