biostudies-literatureUnknown7Khatun MWe have developed an algorithm to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. While generating the trial geometries, a Tabu list is used for storing the information of the already used trial geometries to avoid using the similar trial geometries. In this recursive algorithm, an n-sized cluster is built from the geometries of n-1 clusters. The overall procedure automatically generates many unique minimum energy geometries of clusters with size from 2 up to n using this evolutionary growth strategy. We have used our strategFrontiers in chemistry644https://www.ebi.ac.uk/biostudies/studies/S-EPMC6776882biostudies-literatureA Global Optimizer for Nanoclusters.PMC6776882Khatun MMajumdar RSAnoop AfalseA Global Optimizer for Nanoclusters.We have developed an algorithm to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. While generating the trial geometries, a Tabu list is used for storing the information of the already used trial geometries to avoid using the similar trial geometries. In this recursive algorithm, an n-sized cluster is built from the geometries of n-1 clusters. The overall procedure automatically generates many unique minimum energy geometries of clusters with size from 2 up to n using this evolutionary growth strategy. We have used our strateg2019-01-01T00:00:00Z20192025-04-04T08:30:20.019Z2019-10-18T07:03:03ZS-EPMC67768823161212710.3389/fchem.2019.00644