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

Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

46.6K
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
46.6K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

31.9K
sp3d and sp3d 2 Hybridization
31.9K
Energy Diagrams, Transition States, and Intermediates02:13

Energy Diagrams, Transition States, and Intermediates

16.2K
Free-energy diagrams, or reaction coordinate diagrams, are graphs showing the energy changes that occur during a chemical reaction. The reaction coordinate represented on the horizontal axis shows how far the reaction has progressed structurally. Positions along the x-axis close to the reactants have structures resembling the reactants, while positions close to the products resemble the products.  Peaks on the energy diagram represent stable structures with measurable lifetimes, while...
16.2K
Valence Bond Theory and Hybridized Orbitals02:38

Valence Bond Theory and Hybridized Orbitals

19.0K
According to valence bond theory, a covalent bond results when: (1) an orbital on one atom overlaps an orbital on a second atom, and (2) the single electrons in each orbital combine to form an electron pair. The strength of a covalent bond depends on the extent of overlap of the orbitals involved. Maximum overlap is possible when the orbitals overlap on a direct line between the two nuclei.
A σ bond (single bond in a Lewis structure) is a covalent bond in which the electron density is...
19.0K
Thermodynamic Potentials01:26

Thermodynamic Potentials

783
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
783
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.1K
VSEPR Theory for Determination of Electron Pair Geometries
34.1K

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

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding.

Journal of chemical theory and computation·2026
Same author

Energetics of Noncovalent Interactions of Protein-Ligand Complexes for Drug Discovery.

Journal of chemical information and modeling·2026
Same author

Benchmarking the UMA Foundation Interatomic Potential for Gas-Phase Chemical Kinetics.

The journal of physical chemistry. A·2026
Same author

Sensing the acidity of hydrogen bond networks.

Physical chemistry chemical physics : PCCP·2026
Same author

Prevalence of relapse in pulmonary sarcoidosis: A systematic review and meta-analysis.

Respiratory medicine·2026
Same author

Predicting the Thermodynamic Limits of Metal-Organic Framework Metastability.

Journal of the American Chemical Society·2026
Same journal

Sub1 contributes to heart failure with preserved ejection fraction driven by aging in mice.

Nature communications·2026
Same journal

The BRCA1-A complex restricts replication fork reversal-dependent DNA repair in ATM deficient cells.

Nature communications·2026
Same journal

Signaling downstream of tumor-stroma interaction regulates mucinous colorectal adenocarcinoma apicobasal polarity.

Nature communications·2026
Same journal

Click-polymerized polyenamine membranes for efficient lithium extraction.

Nature communications·2026
Same journal

Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.

Nature communications·2026
Same journal

Proton shuttling at electrochemical interfaces under alkaline hydrogen evolution.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Jun 10, 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

分析 ab initio hessian 从一个深度学习潜力过渡状态优化.

Eric C-Y Yuan1,2, Anup Kumar3, Xingyi Guan1,2

  • 1Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, CA, USA.

Nature communications
|October 14, 2024
PubMed
概括
此摘要是机器生成的。

机器学习准确地识别有机反应中的过渡状态,使用微分神经网络潜力NewtonNet. 这加速了化学机制研究,与传统方法相比,大大降低了计算成本.

更多相关视频

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

8.4K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.2K

相关实验视频

Last Updated: Jun 10, 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
Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

8.4K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.2K

科学领域:

  • 计算化学的计算化学
  • 机器学习在化学中的应用
  • 化学反应动力学 化学反应动力学

背景情况:

  • 识别过渡状态 (点) 对于理解化学反应机制和预测动力障碍至关重要.
  • 传统的方法,如密度函数理论 (DFT) 用于计算潜在能量表面和Hessian,在计算上昂贵.
  • 需要有效和准确的方法来确定复杂有机反应中的过渡状态至关重要.

研究的目的:

  • 开发和验证机器学习 (ML) 方法,以有效识别有机反应中的过渡状态.
  • 为了利用可微分等价神经网络潜力 (NewtonNet) 进行精确的赫西计算.
  • 显著降低与过渡状态搜索相关的计算成本.

主要方法:

  • 在一个有机反应的大数据集上训练一个完全可分化的等价神经网络潜力,NewtonNet.
  • 从训练的牛顿网模型直接导出分析的hessian.
  • 在过渡状态搜索的位点优化的每个步骤中使用ML衍生的Hessian.

主要成果:

  • 来自NewtonNet的ML Hessian强有力的识别了240个看不见的有机反应的过渡状态.
  • 该方法表现出弹性,即使在减弱的初始猜测结构下,也可用于点优化.
  • 与准牛顿式DFT和ML方法相比,趋同的优化步骤减少了2-3倍,表明了显著的计算节省.

结论:

  • 牛顿网为识别有机反应中的过渡状态提供了一种计算效率高和强大的方法.
  • 用ML加速的方法显著降低了研究复杂化学反应机制的障碍.
  • 为数据生成,模型训练和ML过渡状态发现提供了一个自动化工作流.