{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["7"],"submitter":["Khatun M"],"pubmed_abstract":["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 strateg"],"journal":["Frontiers in chemistry"],"pagination":["644"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6776882"],"repository":["biostudies-literature"],"pubmed_title":["A Global Optimizer for Nanoclusters."],"pmcid":["PMC6776882"],"pubmed_authors":["Khatun M","Majumdar RS","Anoop A"],"additional_accession":[]},"is_claimable":false,"name":"A Global Optimizer for Nanoclusters.","description":"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 strateg","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019","modification":"2025-04-04T08:30:20.019Z","creation":"2019-10-18T07:03:03Z"},"accession":"S-EPMC6776882","cross_references":{"pubmed":["31612127"],"doi":["10.3389/fchem.2019.00644"]}}