Table Clusters in current database that were extracted from the literature and their literature sources Low-energy structures from a genetic algorithm Low-energy cluster structures were also identified by means of a genetic algorithm (GA) , an optimization algorithm based on natural evolution in which beneficial characteristics prevail over successive generations. In our implementation, a GA run begins by populating a pool of clusters with random structures and/or seed structures assembled from previous GA run. All cluster energies are evaluated by relaxing the atomic positions using DFT . Child clusters are generated from pool clusters by using two types of operations: crossover, in which parts of each parent cluster are combined to form a child cluster, and mutation, in which a subset of atoms of a cluster structure are randomly moved. If a child cluster has lower energy than the highest-energy pool cluster and is not structurally equivalent to other pool clusters, it replaces the highest-energy cluster of the pool. The cycle continues until the total number of clusters in the GA run is at least 1000. Additional details of the genetic algorithm method can be found in SI section 1 as well as reference .