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相关概念视频

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
X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

3.8K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
3.8K

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相关实验视频

Updated: Jul 1, 2025

Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography
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Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

Published on: April 24, 2018

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基于深度学习的X射线衍射模式的未知化合物的晶体结构赋值

Litao Chen1, Bingxu Wang1, Wentao Zhang1

  • 1School of Advanced Materials, Peking University, Shenzhen Graduate School, Shenzhen 518055, People's Republic of China.

Journal of the American Chemical Society
|March 13, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型使用X射线衍射 (XRD) 模式自动化材料结构分析. 这种计算工具有助于识别未知的化合物结构,加速高吞吐量实验中的材料发现.

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Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source
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相关实验视频

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Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

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Sample Preparation and Transfer Protocol for In-Vacuum Long-Wavelength Crystallography on Beamline I23 at Diamond Light Source
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科学领域:

  • 材料科学
  • 计算化学
  • 晶体学

背景情况:

  • 从实验数据中确定晶体结构对于材料科学至关重要.
  • 目前的方法通常需要广泛的领域专业知识,阻碍了自动化.
  • 自动化结构识别对于高通量材料的发现至关重要.

研究的目的:

  • 开发一个深度学习模型,从实验性表征中自动识别晶体结构类型.
  • 克服手动结构分析和领域专业知识的局限性.
  • 能够有效地分析高吞吐量实验的材料.

主要方法:

  • 使用了结合卷积余神经网络的深度学习模型.
  • 在100种结构类型的6万多个化合物的数据集上训练并验证了模型.
  • 分析模型的可解释性以了解它如何量化结构相似性.

主要成果:

  • 深度学习模型从X射线衍射 (XRD) 模式准确地识别了晶体结构类型.
  • 模型可以在不需要重新培训的情况下整合额外的结构类型.
  • 该模型基于XRD模式中的本地和全球特征量化相似性.

结论:

  • 一种用于自动化材料结构分析的新型计算工具已经开发出来.
  • 这种方法大大减少了对专家知识的依赖.
  • 该工具有助于快速分析通过高吞吐量方法生成的新材料.