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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.2K
VSEPR Theory for Determination of Electron Pair Geometries
34.2K
Classifying Matter by Composition03:35

Classifying Matter by Composition

70.8K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
70.8K
VSEPR Theory and the Basic Shapes02:52

VSEPR Theory and the Basic Shapes

67.8K
Overview of VSEPR Theory
67.8K
Experimental Determination of Chemical Formula02:37

Experimental Determination of Chemical Formula

37.8K
The elemental makeup of a compound defines its chemical identity, and chemical formulas are the most concise way of representing this elemental makeup. When a compound’s formula is unknown, measuring the mass of its constituent elements is often the first step in determining the formula experimentally.
37.8K
Molecular Models02:00

Molecular Models

38.1K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.1K
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

1.2K
An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a low-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.
To...
1.2K

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

Erratum: The crystal structure of dypingite: understanding the long-range disorder. Corrigendum.

Journal of applied crystallography·2026
Same author

The crystal structure of dypingite: understanding the long-range disorder.

Journal of applied crystallography·2026
Same author

A Critical Comparison Among High-Resolution Methods for Spatially Resolved Nano-Scale Residual Stress Analysis in Nanostructured Coatings.

International journal of molecular sciences·2025
Same author

The Area Law of Molecular Entropy: Moving beyond Harmonic Approximation.

Entropy (Basel, Switzerland)·2024
Same author

Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).

Pharmaceuticals (Basel, Switzerland)·2024
Same author

Orientation of reduced graphene oxide in composite coatings.

Nanoscale advances·2024
Same journal

Quantitative analysis of light-induced ion segregation in mixed-halide perovskites.

Journal of applied crystallography·2026
Same journal

Towards machine-learning-based on-the-fly analysis of neutron reflectometry.

Journal of applied crystallography·2026
Same journal

<i>mcstas_gisans</i>: combining ray tracing with the distorted-wave Born approximation using <i>McStas</i> and <i>BornAgain</i> for virtual GISANS experiments.

Journal of applied crystallography·2026
Same journal

Computational methods for automated center determination in electron diffraction patterns.

Journal of applied crystallography·2026
Same journal

Epitaxy of ultrathin Fe<sub>3</sub>O<sub>4</sub> films on SrTiO<sub>3</sub>(001): influence of growth parameters on the formation of coexisting (111)- and (001)-oriented phases.

Journal of applied crystallography·2026
Same journal

Spin excitations near the pressure-induced antiferromagnetic transition in SrCu<sub>2</sub>(BO<sub>3</sub>)<sub>2</sub>.

Journal of applied crystallography·2026
查看所有相关文章

相关实验视频

Updated: Jun 17, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K

从构成中准确地预测空间组.

Vishwesh Venkatraman1, Patricia Almeida Carvalho2,3

  • 1Norwegian University of Science and Technology, 7491Trondheim, Norway.

Journal of applied crystallography
|August 7, 2024
PubMed
概括
此摘要是机器生成的。

通过新的机器学习模型,从化学组成中预测晶体对称性变得更加容易. 这些在广泛的晶体学数据上训练的模型,为预测晶体结构属性提供了更高的准确性.

关键词:
数据集是一组数据集.机器学习是机器学习.预测 预测 预测 预测随机的森林随机的森林空间群 空间群 空间群

更多相关视频

Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

8.9K
X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects
09:16

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

Published on: June 8, 2016

16.2K

相关实验视频

Last Updated: Jun 17, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.1K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

8.9K
X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects
09:16

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

Published on: June 8, 2016

16.2K

科学领域:

  • 晶体学和材料科学 材料科学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 仅仅从化学成分预测晶体对称性是材料科学中的一个重大挑战.
  • 现有的晶体数据库在数据量和分布方面存在局限性,这影响了机器学习模型的预测能力.
  • 准确预测晶体结构对于理解材料特性和发现新化合物至关重要.

研究的目的:

  • 开发和评估机器学习模型,直接从化学成分预测晶体对称性.
  • 通过编制和使用全面的晶体信息数据集来克服现有数据库的局限性.
  • 为预测晶体系统,布拉瓦斯格子,点组和空间组提供可访问的工具.

主要方法:

  • 几乎所有可用的晶体学信息的汇编.
  • 训练和测试多个机器学习模型,包括组合驱动的随机森林分类.
  • 使用大量的化学和结构描述符用于模型训练.

主要成果:

  • 构成驱动的随机森林分类显示了最高的预测性能.
  • 这些模型在预测晶体系统,布拉瓦斯格子,点组和空间组方面取得了显著的准确性.
  • 开发的模型显著优于仅基于流行的晶体数据库的预测.

结论:

  • 机器学习,特别是具有全面描述符的随机森林分类,提供了一种强大的方法,可以从化学组成中预测晶体对称性.
  • 公开可用的软件 (COSY) 为研究人员提供了一个可访问的工具,用于预测无机化合物的晶体学特性.
  • 这项工作通过更有效地预测晶体结构来推进材料发现领域.