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相关概念视频

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
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

17.1K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
17.1K
Molecular Models02:00

Molecular Models

38.3K
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.
38.3K
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
Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

1.9K
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...
1.9K
X-ray Crystallography02:18

X-ray Crystallography

23.9K
The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
23.9K

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相关实验视频

Updated: Jun 28, 2025

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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全球机器学习对分子晶体的潜力.

Ivan Žugec1, R Matthias Geilhufe2, Ivor Lončarić3

  • 1Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU), Donostia-San Sebastián, Spain.

The Journal of chemical physics
|April 16, 2024
PubMed
概括
此摘要是机器生成的。

精确的模拟分子晶体现在是可行的,使用机器学习的原子间潜力. 这些潜能以显著降低的计算成本提供了第一原则的准确性,优于经典的力场.

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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

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Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
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Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

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

  • 计算材料科学 计算材料科学
  • 固态化学 固态化学
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 由于单元细胞的大小,对分子晶体的准确建模具有挑战性.
  • 多态生物经常表现出轻微的能量差异 (约. 1kJ/mol),需要高精度的计算方法.
  • 像密度函数理论 (DFT) 这样的第一原则方法提供了准确性,但在计算上是昂贵的.

研究的目的:

  • 为分子晶体开发精确且计算效率高的机器学习原子间潜力 (MLIP).
  • 创建适用于任何分子晶体的全球MLIP,克服特定系统模型的限制.
  • 为了弥合DFT的准确性和经典力场的效率之间的差距.

主要方法:

  • 培训全球MLIP,使用现有的数据库对分子晶体和分子进行DFT计算.
  • 开发能够高准确度捕捉原子间相互作用的MLIP.
  • 使用大规模的DFT数据,以确保广泛的适用性和准确性.

主要成果:

  • 开发的MLIP的准确性与分子晶体的DFT计算相美.
  • 与传统的古典力场相比,MLIPs表现出优越的性能.
  • 这些潜力与实验基准进行了验证,证实了它们的可靠性.

结论:

  • 机器学习原子间潜能为准确的分子晶体建模提供了一个计算可行的替代方案.
  • 这些MLIP可以在不同的分子晶体中普遍使用,从而促进更广泛的研究.
  • 这种方法显著降低了计算成本,同时保持了高精度,使大规模模拟成为可能.