To address the question posed in the title, we have created, and now report details of, an open-access database of cluster structures with a web-assisted interface and toolkit as part of the WASP@N project. The database establishes a map of connectivities within each structure, the information about which is coded and kept as individual labels, called hashkeys, for the nanoclusters. These hashkeys are the basis for structure comparison within the database, and for establishing a map of connectivities between similar structures (topologies). The database is successfully used as a key element in a data-mining study of (MX)₁₂ clusters of three binary compounds (LiI, SrO and GaAs) of which the database has no prior knowledge. The structures are assessed on the energy landscapes determined by the corresponding bulk interatomic potentials. Global optimisation, using a Lamarckian genetic algorithm, is used to search for low lying minima on the same energy landscape to confirm that the data-mined structures form a representative sample of the landscapes, with only very few structures missing from the close energy neighbourhood of the respective global minima.

中文翻译：

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纳米团簇的最佳或最相关的全球最小值是多少？使用WASP @ N预测，比较和回收群集结构

为了解决标题中提出的问题，作为WASP @ N项目的一部分，我们已经创建了集群结构的开放式访问数据库，并在其中报告了其详细信息，该数据库具有Web辅助界面和工具包。该数据库在每个结构内建立了连通性图，有关其的信息被编码并保存为纳米簇的单独标签，称为哈希键。这些哈希键是数据库内部结构比较以及在相似结构（拓扑）之间建立连接图的基础。该数据库已成功用作（MX）₁₂数据挖掘研究的关键要素三个二进制化合物（LiI，SrO和GaAs）的簇，其数据库没有先验知识。在由相应的整体原子间电势确定的能态上评估结构。使用Lamarckian遗传算法进行全局优化，以在同一能源景观上搜索低洼的最小值，以确认数据挖掘的结构构成了景观的代表性样本，而只有极少的结构在能源密集区附近缺失各自的全局最小值。