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相关概念视频

Determination of Crystal Structures01:29

Determination of Crystal Structures

In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...

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相关实验视频

Updated: Jun 28, 2026

The Automated Crystallography Pipelines at the EMBL HTX Facility in Grenoble
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一个机器学习辅助的晶体结构预测框架,以加速材料发现.

Ran An1,2, Congwei Xie1,2, Dongdong Chu1,2

  • 1Research Center for Crystal Materials, State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions, Xinjiang Key Laboratory of Functional Crystal Materials, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi 830011, China.

ACS applied materials & interfaces
|July 8, 2024
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概括

我们开发了MAXMAT,一种机器学习系统,以加速对新材料的晶体结构预测. 这种方法有效地生成和评估晶体结构,降低计算成本并帮助发现新材料.

关键词:
计算材料的发现发现.晶体结构预测和预测第一个原则是计算计算.功能性材料是一种功能性材料.机器学习是机器学习.

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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
  • 晶体学 晶体学是指结晶学.

背景情况:

  • 由于需要高效的结构采样和精确的能量评估,传统的晶体结构预测方法在计算上昂贵.
  • 开发新材料严重依赖于预测它们的晶体结构和特性.

研究的目的:

  • 开发一种加速系统,用于预测新的晶体结构.
  • 为了降低与晶体结构预测相关的计算成本.

主要方法:

  • 开发了一个机器学习辅助的CRYStalline材料采样系统 (MAXMAT).
  • 使用PyXtal来有效地生成晶体结构.
  • 采用M3GNET,一个机器学习潜力模型,用于快速的能源评估.

主要成果:

  • MAXMAT成功地对TiO2,MgAl2O4和BaBOF3系统进行了晶体结构搜索,证明了准确性和效率.
  • 在LiZnGaS3和CaBOF3系统中预测新的非线性光学材料.
  • 确定了几种具有高性能的热力学合成结构.

结论:

  • MAXMAT显著加速了晶体结构预测过程.
  • 开发的系统有助于发现新材料,包括潜在的非线性光学应用.
  • 这种机器学习辅助的方法为传统方法提供了具有成本效益的替代方案.