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相关概念视频

Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

26.2K
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...
26.2K
Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

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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...
1.7K
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

41.7K
Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
41.7K
Structures of Solids02:22

Structures of Solids

14.0K
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...
14.0K
Molecular Models02:00

Molecular Models

38.0K
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.
38.0K
X-ray Crystallography02:18

X-ray Crystallography

23.8K
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...
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相关实验视频

Updated: Jun 13, 2025

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
07:11

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

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对于晶体结构的自我监督生成模型.

Fangze Liu1,2, Zhantao Chen2,3, Tianyi Liu2,4

  • 1Department of Physics, Stanford University, Stanford, CA 94305, USA.

iScience
|September 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用自主监督学习和图形神经网络的新平台,用于生成无机晶体结构和预测材料特性. 生成对抗网络 (GAN) 提高了模型的可靠性,并有助于理解晶体形成.

关键词:
人工智能的人工智能是人工智能.材料科学 材料科学 材料科学

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相关实验视频

Last Updated: Jun 13, 2025

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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Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
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Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 人工智能的人工智能

背景情况:

  • 自然语言处理的进步激发了新的机器学习方法.
  • 产生可靠的无机晶体结构对于材料发现至关重要.
  • 预测材料特性需要高效和准确的模型.

研究的目的:

  • 为无机晶体结构的生成模型开发一个统一的平台.
  • 为了能够有效地适应下游任务,如材料属性预测.
  • 提高培训期间生成结构的可靠性评估.

主要方法:

  • 利用自我监督学习和等价图形神经网络.
  • 采用生成对抗网络 (GAN) 具有可靠性评估的成本效益区分器.
  • 在优化晶体结构和分组化学上相似的元素方面证明了模型实用性.

主要成果:

  • 成功生成了无机晶体结构,并预测了材料特性.
  • 通过基于GAN的可靠性评估显著提高模型性能.
  • 展示了模型在没有外部数据的情况下优化结构和理解晶体形成的能力.

结论:

  • 开发的平台为材料科学中的机器学习提供了一个新的视角.
  • 生成模型可以有效地帮助理解无机晶体的形成和特性.
  • 这项工作为进一步探索AI在发现和设计新材料方面铺平了道路.