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相关概念视频

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

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 – the...
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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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Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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通过遗传算法和机器学习加速有机晶体结构预测.

Amit Kadan1, Kevin Ryczko1, Andrew Wildman1

  • 1Good Chemistry Company, 1285 W Pender Street, Vancouver, British Columbia V6E 4B1, Canada.

Journal of chemical theory and computation
|December 7, 2023
PubMed
概括

我们开发了一种用于有机晶体结构预测的新管道. 它使用机器学习有效地识别稳定的晶体结构,达到80%的成功率.

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科学领域:

  • 计算化学是一种计算化学.
  • 材料科学 是一种材料科学.
  • 晶体学 晶体学是指结晶学.

背景情况:

  • 有机晶体结构预测 (CSP) 对于理解材料特性至关重要.
  • 准确的CSP需要有效的选和优化晶体候选方法.

研究的目的:

  • 为有机晶体结构预测提供一个高吞吐量,端到端的管道.
  • 为了利用神经网络的潜力,进行高效的选和结构放松.
  • 评估管道不同阶段的性能.

主要方法:

  • 一个两阶段的管道,结合随机搜索和遗传算法 (GA) 优化.
  • 使用神经网络潜力进行能量计算.
  • 从CSP盲测试中对21个分子进行验证.

主要成果:

  • 随机搜索单独实现了约50%的成功率.
  • 完整的管道与GA优化发现匹配的目标约80%.
  • 该GA方法需要10-100倍小的初始种群.
  • 使用ANI模型的管道实现了约60%的成功率.

结论:

  • 开发的管道显著提高了有机CSP的效率和成功率.
  • 机器学习模型可以接近初始方法的准确性,同时保持效率.
  • 这种工具有可能加速材料的发现.