Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

4.7K
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...
4.7K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

30.5K
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...
30.5K
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

48.0K
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,...
48.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures.

Journal of the American Chemical Society·2026
Same author

PhAI: A deep-learning approach to solve the crystallographic phase problem.

Science (New York, N.Y.)·2024
Same author

Attraction between Like Charged Ions in Ionic Liquids: Unveiling the Enigma of Tetracyanoborate Anions.

The journal of physical chemistry letters·2024
Same author

Orthorhombic charge density wave on the tetragonal lattice of EuAl<sub>4</sub>.

IUCrJ·2022
Same author

Single-crystal-to-single-crystal phase transitions of commensurately modulated sodium saccharinate 1.875-hydrate.

IUCrJ·2021
Same author

On the puzzling case of sodium saccharinate 1.875-hydrate: structure description in (3+1)-dimensional superspace.

Acta crystallographica Section B, Structural science, crystal engineering and materials·2020
Same journal

Case study of using the single-atom R1 method to solve a small protein structure.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

Beyond complementarity: a reverse-engineering framework for de novo crystal structure determination from EXAFS.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

Crystallography in Open Science and its open educational resources.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

From atoms to a data bank: optimizing transferability of electron-density symmetry.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

Twenty-Sixth General Assembly and International Congress of Crystallography, Melbourne, Australia, 22-29 August 2023.

Acta crystallographica. Section A, Foundations and advances·2026
Same journal

MIDAS: a quantitative framework for high-energy diffraction microscopy. Part II: accuracy, robustness and best practices.

Acta crystallographica. Section A, Foundations and advances·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K

关于人工晶体结构的生成,以解决深度学习的相位问题.

Džonatans Miks Melgalvis1, Toms Rekis2

  • 1Faculty of Medicine and Life Sciences, University of Latvia, Jelgavas iela 1, Riga LV1004, Latvia.

Acta crystallographica. Section A, Foundations and advances
|November 11, 2025
PubMed
概括
此摘要是机器生成的。

人工晶体结构帮助神经网络解决晶体相问题. 在新的人工数据上重新训练PhAI网络,大大提高了它分析更大的单元细胞结构的能力.

关键词:
人工晶体结构的人工晶体结构深度学习是一种深度学习.阶段问题问题阶段问题

更多相关视频

Optimization of Crystal Growth for Neutron Macromolecular Crystallography
12:29

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

5.9K
Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography
18:45

Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography

Published on: September 2, 2012

25.6K

相关实验视频

Last Updated: Jan 11, 2026

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening
14:04

Derivatization of Protein Crystals with I3C using Random Microseed Matrix Screening

Published on: January 16, 2021

5.0K
Optimization of Crystal Growth for Neutron Macromolecular Crystallography
12:29

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

5.9K
Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography
18:45

Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography

Published on: September 2, 2012

25.6K

科学领域:

  • 晶体学 晶体学是指结晶学.
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 解决结晶学阶段问题对于确定晶体结构至关重要.
  • 神经网络为相位检索提供了一个有希望的方法.
  • 生成现实的人工晶体结构对于培养这些网络至关重要.

研究的目的:

  • 介绍和讨论用于神经网络训练的人工晶体结构生成的方法.
  • 评估PhAI神经网络在使用新型人工数据集进行重新训练后对实验数据的性能.

主要方法:

  • 结构生成涉及采样单元细胞参数和原子位置.
  • 格子基向量是从采样的单元细胞体积生成的.
  • 使用数据库数据生成类似分子的碎片来指导原子的放置,补充随机方法.

主要成果:

  • 通过使用各种人工数据集对PhAI神经网络进行了基准测试和重新训练.
  • 用一种新型的人工数据重新训练PhAI显示出显著的改善.
  • 改进的模型显示了在更大的单元细胞结构中解决相位问题的增强概括性.

结论:

  • 人工数据生成是一种可行的策略,用于提高神经网络在晶体学中的性能.
  • 开发的方法使可扩展生成的晶体结构用于培训.
  • 该研究强调了人工智能在推进晶体结构确定方面的潜力.