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相关概念视频

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
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 12, 2025

X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects
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X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects

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通过生成机器学习从粉末衍射模式确定晶体结构

Eric A Riesel1, Tsach Mackey1, Hamed Nilforoshan2

  • 1Department of Chemistry, Massachusetts Institute of Technology; Cambridge, Massachusetts 02139, United States.

Journal of the American Chemical Society
|September 19, 2024
PubMed
概括
此摘要是机器生成的。

一个新的生成机器学习模型现在可以直接从粉末X射线衍射 (PXRD) 数据中解决晶体结构. 这一突破通过自动化新型和高压材料的结构确定加速了材料的发现.

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Last Updated: Jun 12, 2025

X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects
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Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
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科学领域:

  • 材料科学
  • 晶体学
  • 机器学习

背景情况:

  • 粉末X射线衍射 (PXRD) 对于材料的表征至关重要,但完整的结构确定是具有挑战性和耗时的.
  • 目前用于PXRD分析的机器学习 (ML) 方法只能预测部分结构信息.

研究的目的:

  • 从实验PXRD数据直接开发一个开创性的生成ML模型来确定完整的晶体结构.
  • 在PXRD分析中克服当前ML方法的局限性.

主要方法:

  • 开发了一个能够分析PXRD模式的生成机器学习模型.
  • 在RRUFF和材料项目数据库中的模拟和实验衍射数据上验证了模型.
  • 应用该模型来确定以前未报告的结构从粉末衍射文件和新合成的高压材料.

主要成果:

  • 在模拟和实验PXRD数据上,ML模型实现了最先进的性能.
  • 成功预测了134个实验模式和数千个模拟模式的晶体结构.
  • 包括NaCu2P2,Ca2MnTeO6和Rh3Bi等高压化合物的未报告结构.

结论:

  • 开发的生成ML模型可以从PXRD数据中获得完整的晶体结构解决方案.
  • 这种方法显著加速了材料的发现,特别是在防止单晶生长的条件下.
  • 该模型准备推进自动化材料发现管道并探索新的化学领域.