Jove
Visualize
联系我们

相关概念视频

Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

2.7K
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...
2.7K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

28.0K
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...
28.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

36.2K
VSEPR Theory for Determination of Electron Pair Geometries
36.2K
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

44.8K
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,...
44.8K
X-ray Crystallography02:18

X-ray Crystallography

24.2K
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...
24.2K
Network Covalent Solids02:18

Network Covalent Solids

14.6K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
14.6K

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

ChemXDyn: Dynamics-Informed Species and Reaction Detection Methodology from Atomistic Simulations.

Journal of chemical theory and computation·2026
Same author

Dynamic surface reconstruction governs the hydrogen evolution activity of Mo<sub>2</sub>C electrocatalysts in alkaline media.

Materials horizons·2026
Same author

O<sub>2</sub>-assisted methane oxidation on single-atom Pd@SSZ-13: a combined first-principles and microkinetic study.

Physical chemistry chemical physics : PCCP·2026
Same author

Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali-Metal-Ion Battery Materials.

Small methods·2026
Same author

Real-Space Methods for Ab Initio Modeling of Surfaces and Interfaces under External Potential Bias.

Journal of chemical theory and computation·2025
Same author

A Hybrid Machine Learning Framework for Predicting Hydrogen Storage Capacities in Metal Hydrides: Unsupervised Feature Learning with Deep Neural Networks.

ACS applied materials & interfaces·2025
Same journal

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026
Same journal

Graph-Based Generation and Reduction of Complex Chemical Reaction Networks.

Journal of chemical information and modeling·2026
Same journal

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.

Journal of chemical information and modeling·2026
Same journal

Machine Learning-Driven Discovery of Indole/Oxoindole-Piperazine Scaffolds as Dual MAO-B/Sig-1R Ligands for Neurodegenerative Disorders.

Journal of chemical information and modeling·2026
Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026
Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026
查看所有相关文章
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

相关实验视频

Updated: Sep 17, 2025

Optimization of Crystal Growth for Neutron Macromolecular Crystallography
12:29

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

5.6K

强大的轻量级图形神经网络框架加速晶体结构预测.

Rushikesh Pawar1, Ashish Rout1, Satadeep Bhattacharjee2

  • 1Department of Computational and Data Sciences, Indian Institute of Science, Bangalore 560012 Karnataka, India.

Journal of chemical information and modeling
|June 30, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用图形神经网络 (GNN) 的强大的晶体结构预测框架. 它提高了材料发现的预测准确性和计算效率.

更多相关视频

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
06:19

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Published on: March 10, 2023

4.8K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.9K

相关实验视频

Last Updated: Sep 17, 2025

Optimization of Crystal Growth for Neutron Macromolecular Crystallography
12:29

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

5.6K
Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
06:19

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Published on: March 10, 2023

4.8K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.9K

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 图形神经网络 (GNN) 越来越多地用于晶体结构预测 (CSP).
  • 现有的基于GNN的CSP框架在稳定性和计算效率方面存在局限性.
  • 在CSP中,GNN模型对重量初始化的敏感性是一个关键的问题,但经常被忽视.

研究的目的:

  • 开发一个基于GNN的稳健和计算效率高的晶体结构预测框架.
  • 解决GNN对重量初始化的敏感性,并改进模型选择.
  • 通过数据增强和预训练策略,提高CSP的GNN的性能.

主要方法:

  • 在结构性搜索中采用无衍生品优化方法.
  • 使用监督的图形神经网络 (GNN) 作为能量评估器.
  • 引入了一个模型选择框架,以确定适合CSP的GNN模型.
  • 实施了使用未放松结构的数据增强策略.
  • 探索了无监督的GNN预训,有或没有增强.

主要成果:

  • 开发了一个模型选择框架,以始终确定适合CSP的GNN模型.
  • 证明了用未放松结构增强数据可以提高GNN的性能.
  • 展示了未经监督的预训可以增强基于GNN的CSP.
  • 使用轻量级CGCNN架构实现了与复杂GNN可比的性能.
  • 验证了框架的有效性和计算效率.

结论:

  • 拟议的框架为预测晶体结构提供了一种强大且计算效率高的方法.
  • 为GNN模型选择和数据增强开发的方法是可通用的.
  • 这项工作为新和高通量晶体结构预测铺平了道路.
  • 像CGCNN这样的轻量级GNN架构可以在CSP中实现竞争性性能.
  • 这些发现有助于推进材料科学中的机器学习应用.