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

Thermodynamic Potentials01:26

Thermodynamic Potentials

825
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...
825
Van der Waals Interactions01:24

Van der Waals Interactions

63.8K
Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
63.8K
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

32.2K
sp3d and sp3d 2 Hybridization
32.2K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

47.0K
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...
47.0K
Atomic Orbitals02:44

Atomic Orbitals

33.5K
An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.
33.5K
The Energies of Atomic Orbitals03:21

The Energies of Atomic Orbitals

23.9K
In an atom, the negatively charged electrons are attracted to the positively charged nucleus. In a multielectron atom, electron-electron repulsions are also observed. The attractive and repulsive forces are dependent on the distance between the particles, as well as the sign and magnitude of the charges on the individual particles. When the charges on the particles are opposite, they attract each other. If both particles have the same charge, they repel each other.
23.9K

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

Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials.

Journal of chemical theory and computation·2026
Same author

Bottom-up synthesis of molecular nanodiamond from nanographene.

Nature·2026
Same author

Accelerated Reaction Exploration across Scales: A Hybrid Operando and Modeling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide.

Journal of the American Chemical Society·2026
Same author

Limitations of Cluster-Trained MLIPs for Liquid Density and Diffusivity.

Journal of chemical theory and computation·2026
Same author

Computing Solvation Free Energies of Small Molecules with Experimental Accuracy.

Journal of the American Chemical Society·2026
Same author

How accurate are DFT forces? Unexpectedly large uncertainties in molecular datasets.

The Journal of chemical physics·2025
Same journal

Vision language models for scientific image analysis: an evaluation highlighting opportunities and challenges.

npj computational materials·2026
Same journal

Cavity control of multiferroic order in single-layer NiI<sub>2</sub>.

npj computational materials·2026
Same journal

Extraction of the self energy and Eliashberg function from angle resolved photoemission spectroscopy using the xARPES code.

npj computational materials·2026
Same journal

Equivariant electronic Hamiltonian prediction with many-body message passing.

npj computational materials·2026
Same journal

Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties.

npj computational materials·2026
Same journal

System-conditioned reparameterization of the SCAN functional for accurate bandgaps: from analytical constraints to machine learning.

npj computational materials·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K

对于数据驱动的原子间潜力的超活性学习.

Cas van der Oord1, Matthias Sachs2, Dávid Péter Kovács1

  • 1University of Cambridge, Cambridge, CB2 1PZ UK.

npj computational materials
|April 26, 2024
PubMed
概括
此摘要是机器生成的。

超活性学习 (HAL) 加快了对原子间潜力的训练数据的创建. 这种方法可以快速生成像AlSi10和PEG聚合物这样的材料的精确潜力.

关键词:
原子模型是原子模型.计算方法 计算方法

更多相关视频

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

8.2K
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.2K

相关实验视频

Last Updated: Jun 27, 2025

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K
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

8.2K
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.2K

科学领域:

  • 计算材料科学 计算材料科学
  • 机器学习在化学中的应用
  • 原子模拟的原子模拟.

背景情况:

  • 原子间电位接近复杂的量子力学计算.
  • 产生准确的培训数据是开发这些潜力的瓶.
  • 现有的方法需要大量的计算资源来生成数据库.

研究的目的:

  • 为加速培训数据库组装引入超活性学习 (HAL).
  • 为了证明HAL在创建数据驱动的原子间潜力的效率.
  • 为了验证HAL生成的材料性能潜力的准确性.

主要方法:

  • 开发了一个HAL框架,将偏差术语与物理样本 (例如分子动力学) 集成在一起.
  • 应用HAL生成AlSi10合金和聚乙烯糖醇 (PEG) 聚合物的训练配置.
  • 利用生成的数据来训练原子集群扩张 (ACE) 的原子间潜力.

主要成果:

  • HAL显著减少了培训数据库组装所需的时间和数据.
  • 由HAL生成的ACE潜力准确地预测了AlSi10和PEG的宏观性质 (化温度,密度).
  • 潜能从最小的初始配置开始,达到接近实验值的准确性.

结论:

  • HAL是快速,自动化培训数据库生成的有效策略.
  • 这种方法可以有效地开发精确的数据驱动的原子间潜力.
  • 在计算材料科学和聚合物建模中,HAL具有广泛的应用.