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

Distribution of Molecular Speeds01:27

Distribution of Molecular Speeds

3.8K
The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...
3.8K
Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

28.4K
Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
28.4K
Molecular Kinetic Energy01:21

Molecular Kinetic Energy

5.0K
The word "gas" comes from the Flemish word meaning "chaos," first used to describe vapors by the chemist J. B. van Helmont. Consider a container filled with gas, with a continuous and random motion of molecules. During collisions, the velocity component parallel to the wall is unchanged, and the component perpendicular to the wall reverses direction but does not change in magnitude. If the molecule’s velocity changes in the x-direction, then its momentum is changed.
5.0K
Van der Waals Equation01:10

Van der Waals Equation

3.9K
The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
First, the attractive forces between molecules, which are stronger at higher densities and reduce the pressure, are considered by adding to the pressure a term equal to the square of the molar density multiplied by a positive coefficient a. Second, the volume...
3.9K
Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy03:07

Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy

27.1K
The kinetic molecular theory qualitatively explains the behaviors described by the various gas laws. The postulates of this theory may be applied in a more quantitative fashion to derive these individual laws.
27.1K
Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation04:01

Real Gases: Effects of Intermolecular Forces and Molecular Volume Deriving Van der Waals Equation

34.3K
Thus far, the ideal gas law, PV = nRT, has been applied to a variety of different types of problems, ranging from reaction stoichiometry and empirical and molecular formula problems to determining the density and molar mass of a gas. However, the behavior of a gas is often non-ideal, meaning that the observed relationships between its pressure, volume, and temperature are not accurately described by the gas laws. 
34.3K

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

Faster Molecular Dynamics with Neural Network Potentials via Distilled Multiple Time-Stepping and Nonconservative Forces.

Journal of chemical theory and computation·2026
Same author

Dual-LAO for calculating fast and robust relative binding free energies of simple and complex transformations.

Communications chemistry·2026
Same author

Accelerating Molecular Dynamics Simulations with Foundation Neural Network Models Using Multiple Time Steps and Distillation.

The journal of physical chemistry letters·2026
Same author

Quantum speedup for nonreversible Markov chains.

Nature communications·2025
Same author

Probing the partition function for temperature-dependent potentials with nested sampling.

The Journal of chemical physics·2025
Same author

Erratum: "Position-dependent memory kernel in generalized Langevin equations: Theory and numerical estimation" [J. Chem. Phys. 156, 244105 (2022)].

The Journal of chemical physics·2025

相关实验视频

Updated: May 24, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.4K

分子动力学的速度跳跃

Nicolaï Gouraud1,2,3, Louis Lagardère1,3, Olivier Adjoua1

  • 1Sorbonne Université, CNRS, LCT UMR 7616, Paris 75005, France.

Journal of chemical theory and computation
|March 6, 2025
PubMed
概括
此摘要是机器生成的。

我们介绍了速度跳跃 (JUMP) 方法,这是一个新的分子动力学集成器. JUMP通过在随机时间重新采样速度来加速模拟,从而保持基本的动态特性.

更多相关视频

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
08:48

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

Published on: April 28, 2022

1.7K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.0K

相关实验视频

Last Updated: May 24, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

4.4K
High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
08:48

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

Published on: April 28, 2022

1.7K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.0K

科学领域:

  • 计算化学计算化学
  • 分子动力学模拟模型
  • 统计力学 统计力学

背景情况:

  • 经典分子动力学模拟经常面临由于远程相互作用的计算瓶.
  • 对于大型系统来说,Langevin动力学等现有方法在计算上可能是昂贵的.
  • 多个时间步骤的方法提供了加快速度,但可能会遭受共振问题.

研究的目的:

  • 为了引入一个新的分子动力学集成器类,速度跳跃 (JUMP) 方法.
  • 开发一种加速模拟的方法,同时保持采样和动态的准确性.
  • 通过用随机速度重新抽样取代昂贵的计算来提高分子动力学的计算效率.

主要方法:

  • 开发了速度跳跃 (JUMP) 方法,这是一个混合模型,结合了朗格温扩散和断片决定性的马尔科夫过程.
  • 取代了长距离对交互的计算,以随机间隔的速度重新采样.
  • 集成的JUMP与经典的多步时间方法 (JUMP-RESPA,JUMP-RESPA1).
  • 在GPU加速的Tinker-HP包中实现了JUMP集成器.

主要成果:

  • JUMP方法显著加速了分子动力学模拟.
  • 证明了关键采样和动态性质的保存,包括扩散常数.
  • 与多个时间步骤方法的集成进一步提高了计算速度.
  • 速度跳跃的随机性质有助于避免其他方法中常见的共振问题.
  • 与其在GPU上的BAOAB同行相比,JUMP集成器显示出更高的性能.

结论:

  • 速度跳跃 (JUMP) 方法为分子动力学模拟提供了一个计算效率高的替代方案.
  • JUMP提供了一个强大的框架,可以在不影响准确性的情况下加速模拟.
  • 在Tinker-HP中的实现证明了GPU的实际适用性和显著的性能增长.