PyAR

PyAR (Python Assisted Recursion) is a software tool for globally optimizing nanoclusters. Its main objective is to automatically generate the global minimum and other low-energy minima structures of nanoclusters.

The algorithm implemented in PyAR consists of two main components: the generation of trial geometries and the gradient-based local optimization of these trial geometries. Generating trial geometries is a crucial step in the global optimization process. PyAR employs a Tabu list, which stores information about previously used trial geometries to avoid redundant calculations and improve efficiency.

The algorithm's recursive nature allows for systematic exploration of the potential energy surface, enabling the identification of the global minimum and other low-energy minima structures. PyAR utilizes a combination of stochastic and deterministic approaches to effectively navigate the complex energy landscape of nanoclusters.

By automating the global optimization process, PyAR simplifies finding the most stable configurations of nanoclusters. This tool is particularly useful for researchers in the field of nanoscience and materials science, as it provides valuable insights into the structural and energetic properties of nanoclusters.