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相关概念视频

Metallic Solids02:37

Metallic Solids

18.4K
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 malleability....
18.4K
Structures of Solids02:22

Structures of Solids

14.1K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
14.1K
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

2.3K
2.3K
Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

2.8K
Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
2.8K

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瓦斯:一种高合金,用于机器学习的短程顺序结构描述器.

Jiaheng Liu1, Pengbo Wang1, Jun Luan1

  • 1State Key Laboratory of Advanced Special Steel & Shanghai Key Laboratory of Advanced Ferrometallurgy & School of Materials Science and Engineering, Shanghai University,99 Shangda Road, Baoshan District, Shanghai 200444, China.

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|July 24, 2024
PubMed
概括

研究人员开发了一个新的描述器,Voronoi分析和香农 (VASE),以有效预测高合金 (HEAs) 的特性. 这种机器学习方法准确地模拟了对HEA性能至关重要的短程订单 (SRO) 结构.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学
  • 合金设计设计 合金设计

背景情况:

  • 高合金 (HEA) 的短程顺序 (SRO) 显著影响其性能.
  • 密度函数理论 (DFT) 计算对于研究SRO至关重要,但在计算上是密集的.
  • 机器学习 (ML) 为在HEA研究中快速估计DFT结果提供了一条途径.

研究的目的:

  • 提出一种新的描述符,用于在高等教育机构中描述SRO.
  • 提高使用ML预测HEA属性的准确性和效率.
  • 为了研究超越构成和相互作用的原子空间布局.

主要方法:

  • 沃罗诺伊分析和香农 (VASE) 描述符的开发.
  • 将VASE与用于财产预测的机器学习模型集成.
  • 将VASE与库伦矩阵和辐射分布函数等现有描述符进行比较.

主要成果:

  • VASE描述器准确地捕获了HEAs中的原子空间排列信息.
  • 用VASE描述器训练的ML模型在预测FeCoNiAlTiCu系统的形成能量方面表现出卓越的准确性.
  • 基于VASE的模型实现了对未放松结构的最佳预测性能,误差为24.06 meV/atom.

结论:

  • VASE描述符提供了原子排列的有效表示,这对于理解HEAs中的SRO至关重要.
  • 这种新的描述符显著提高了机器学习模型对HEA属性的预测能力.
  • 瓦斯是一种强大的工具,可以促进复杂合金系统中SRO现象的研究.