# Simultaneous prediction of atomic structure and stability of nanoclusters in a wide area of compositions

## TL;DR

This paper introduces a universal, efficient method for predicting atomic structures and stability of nanoclusters across a wide compositional space, enabling comprehensive first-principles studies.

## Contribution

The authors develop a joint evolutionary algorithm that accelerates cluster structure prediction by leveraging structural similarities, significantly improving speed and coverage.

## Key findings

- Speedup of up to 50 times over existing methods
- First-principles global optimization of 315 Si-O clusters
- Discovery of both expected and novel stable clusters

## Abstract

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.