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相关概念视频

Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

4.7K
Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
4.7K
Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

3.7K
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...
3.7K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

30.6K
Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
30.6K
Recrystallization: Solid–Solution Equilibria01:10

Recrystallization: Solid–Solution Equilibria

2.2K
Recrystallization is a purification technique used to separate impurities from solid compounds. In this technique, no chemical reactions occur. Instead, it exploits physical properties only, specifically, the solubility differences between the desired compound and impurities, either at a single temperature or at different temperatures, and under other selected conditions. The solid-solution equilibrium (solubility equilibrium) of each component in the solution represents a binary phase...
2.2K
Structures of Solids02:22

Structures of Solids

17.4K
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...
17.4K
Metallic Solids02:37

Metallic Solids

20.5K
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....
20.5K

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相关实验视频

Updated: Jan 14, 2026

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

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CrystalFlow:一种基于流量的生成模型,用于晶体材料.

Xiaoshan Luo1,2, Zhenyu Wang1,3, Qingchang Wang1

  • 1Key Laboratory of Material Simulation Methods and Software of Ministry of Education, College of Physics, Jilin University, Changchun, PR China.

Nature communications
|October 20, 2025
PubMed
概括
此摘要是机器生成的。

新的深度学习模型CrystalFlow能够高效地生成高质量的晶体结构. 这种基于流量的生成模型提供了与最先进的方法可比的性能,并且比扩散模型快得多.

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On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature
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On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature

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Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
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Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

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相关实验视频

Last Updated: Jan 14, 2026

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

8.9K
On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature
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On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature

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Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
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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
  • 人工智能的人工智能

背景情况:

  • 深度学习生成模型显示出探索晶体材料配置的前景.
  • 目前的应用有限,在模拟复杂的晶体结构方面面临挑战.
  • 现有的方法需要大量的计算资源和时间.

研究的目的:

  • 介绍CrystalFlow,一种基于流量的新型生成模型用于晶体结构生成.
  • 为了解决模拟格子参数,原子坐标和原子类型的具体挑战.
  • 为了使材料配置空间的高效和数据驱动的探索.

主要方法:

  • 使用连续规范化流量和条件流量匹配.
  • 采用基于图形的等价神经网络架构.
  • 嵌入了对称感知数据表示,以实现高效的学习.

主要成果:

  • 晶体流实现了与最先进的生成模型可比的性能.
  • 证明了多功能条件生成能力,例如在特定条件下预测结构.
  • 展示了卓越的计算效率,比基于扩散的模型快一个数量级.

结论:

  • 晶体流提供了一种高效和有效的方法,用于生成高质量的晶体结构.
  • 该模型的架构促进了数据高效学习和条件生成.
  • 在将深度学习应用于材料发现方面取得了重大进展.