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A New Kind of Atlas of Zeolite Building Blocks

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DataCite Commons2026-03-12 更新2025-04-16 收录
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https://archive.materialscloud.org/doi/10.24435/materialscloud:2019.0079/v1
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We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether a more general representation of the atomic structure, furnished by the smooth overlap of atomic position (SOAP) method, is required to capture accurately structure–property relations. We assessed the quality of each descriptor by machine-learning the molar energy and volume for each hypothetical framework in the dataset. We have found that a SOAP representation with a cutoff length of 6 Å, which goes beyond near-neighbor tetrahedra, best describes the structural diversity in the Deem database by capturing relevant interatomic correlations. Kernel principal component analysis shows that SOAP maintains its superior performance even when reducing its dimensionality to those of the classical descriptors and that the first three kernel principal components capture the main variability in the dataset, allowing a 3D point cloud visualization of local environments in the Deem database. This "cloud atlas" of local environments was found to show good correlations with the contribution of a given motif to the density and stability of its parent framework. Local volume and energy maps constructed from the SOAP/machine learning analyses provide new images of zeolites that reveal smooth variations of local volumes and energies across a given framework and correlations between the contributions to volume and energy associated with each atom-centered environment.

我们分析了Deem数据库中假设沸石的结构基序,旨在探究该数据库中发现的结构多样性能否通过距离、角度及环大小等经典描述符得到良好表征,抑或是否需要借助原子位置平滑重叠(SOAP)方法所提供的更通用原子结构表示,才能准确捕捉结构-性质关系。通过对数据集中每个假设骨架的摩尔能量与体积进行机器学习,我们评估了各描述符的质量。研究发现,截断长度为6 Å的SOAP表示(其范围超越近邻四面体)能捕捉到相关的原子间关联,从而最优描述Deem数据库中的结构多样性。核主成分分析结果显示,即便将SOAP表示的维度降至经典描述符的水平,其仍保持优越性能;且前三个核主成分可捕捉数据集中的主要变异,进而实现Deem数据库中局部环境的三维点云可视化。这种局部环境的“云图”被发现与特定基序对其母骨架密度及稳定性的贡献存在良好相关性。基于SOAP/机器学习分析构建的局部体积与能量图,为沸石提供了新的图像表征,揭示了特定骨架中局部体积与能量的平滑变化,以及原子中心环境对体积和能量贡献之间的相关性。
提供机构:
Materials Cloud
创建时间:
2019-11-12
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