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Novel Approach for Clustering Zeolite Crystal Structures

机译:分子筛分子筛簇聚的新方法

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Informatics approaches play an increasingly important role in the design of new materials. In this work we apply unsupervised statistical learning for identifying four framework-type attractors of zeolite crystals in which several of the zeolite framework types are grouped together. Zeolites belonging to these super-classes manifest important topological, chemical and physical similarities. The zeolites form clusters located around four core framework types: LTA, FAU, MFI and the combination of EDI, HEU, LTL and LAU. Clustering is performed in a 9-dimensional space of attributes that reflect topological, chemical and physical properties for each individual zeolite crystalline structure. The implemented machine learning approach relies on hierarchical top-down clustering approach and the expectation maximization method.
机译:信息学方法在新材料的设计中起着越来越重要的作用。在这项工作中,我们采用无监督的统计学习方法来确定四种沸石骨架类型的沸石晶体吸引子,其中几种沸石骨架类型组合在一起。属于这些超类的沸石表现出重要的拓扑,化学和物理相似性。沸石形成围绕四个核心框架类型的簇:LTA,FAU,MFI以及EDI,HEU,LTL和LAU的组合。在9维空间的属性中执行聚类,这些属性反映了每个单独的沸石晶体结构的拓扑,化学和物理特性。实施的机器学习方法依赖于分层自上而下的聚类方法和期望最大化方法。

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