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相关概念视频

Ionic Crystal Structures02:42

Ionic Crystal Structures

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Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...
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Predicting Molecular Geometry02:27

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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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VSEPR Theory02:37

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Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...
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VSEPR Theory and the Basic Shapes02:52

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Overview of VSEPR Theory
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Metallic Solids02:37

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Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and...
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基于Cu-S的化合物使用周期表表示和构成Wasserstein距离集群.

Shuyan Hao1,2, Ting Xia2, Ruizhi Zhang3

  • 1Key Laboratory of Computing Power Network and Information Security, Shandong Computer Science Center (National Supercomputing Center in Jinan), Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250013, Shandong, P. R. China.

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概括

我们开发了一种使用瓦瑟斯坦距离测量材料组成相似性的新方法. 这种方法有助于通过聚集现有化合物来发现具有相似性质的新材料,帮助材料数据挖掘.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 数据挖掘 数据挖掘

背景情况:

  • 分析大型材料数据库对于发现新材料至关重要.
  • 量化组成和结构相似性是材料数据挖掘的关键.
  • 现有的方法可能无法完全捕捉微妙的组成关系.

研究的目的:

  • 引入一种用于测量化学化合物之间的组成相似性的新方法.
  • 用铜-硫 (Cu-S) 化合物的数据集来证明这种方法的有效性.
  • 通过无监督聚类,促进发现具有理想性质的新材料.

主要方法:

  • 利用二维的瓦瑟斯坦距离 (地球移动器距离) 来量化基于周期表表示的组成相似性.
  • 综合的组成相似性与使用局部结构顺序参数的几何相似性.
  • 采用了基于密度的应用与噪声 (DBSCAN) 的空间聚类算法,用于无监督的Cu-S化合物的聚类.
  • 使用晶体结构可视化分析聚类组以获得化学洞察力.

主要成果:

  • 从无机晶体结构数据库 (ICSD) 成功将1586个Cu-S化合物分组成86个不同的组.
  • 确定了一组含有稀土的多层Cu-S化合物作为潜在的热电材料.
  • 通过可视化结构的化学见解,验证了瓦瑟斯坦距离度量和聚类方法的有效性.

结论:

  • 拟议的瓦瑟斯坦距离方法提供了一种有效的方法来测量材料数据挖掘的组成相似性.
  • 基于组合的组成和几何相似性的无监督聚类有助于理解材料数据集.
  • 这种方法可以加速发现具有相似性质的新材料,特别是用于热电等应用.