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

Atomic Absorption Spectroscopy: Atomization Methods01:25

Atomic Absorption Spectroscopy: Atomization Methods

1.6K
Atomic Absorption Spectroscopy (AAS) atomizes samples through flame atomization or electrothermal atomization. Flame atomization typically involves a nebulizer and spray chamber assembly to combine the sample with a fuel–oxidant mixture, creating a fine aerosol mist that enters a burner. Typically, the fuel and oxidant are combined in an approximately stoichiometric ratio. However, for atoms that are easily oxidized, a fuel-rich mixture may be more advantageous. Only about 5% of the...
1.6K
Poisson's Ratio01:23

Poisson's Ratio

1.1K
Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
1.1K
Molecular Models02:00

Molecular Models

43.7K
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.
43.7K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.9K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.9K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

67.5K
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...
67.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.9K

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

Interplay of structure and dynamics in solid polymer electrolytes: a molecular dynamics study of LiPF<sub>6</sub>/polypropylene carbonate.

Physical chemistry chemical physics : PCCP·2026
Same author

Generalization of long-range machine learning potentials in complex chemical spaces.

Digital discovery·2026
Same author

Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling.

ACS nano·2026
Same author

Open-boundary molecular dynamics of red blood cell suspensions.

The Journal of chemical physics·2026
Same author

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.

Journal of chemical information and modeling·2026
Same author

Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.

Journal of chemical theory and computation·2025
Same journal

Anharmonic phonons via quantum thermal bath simulations.

The Journal of chemical physics·2026
Same journal

Quantum simulation of alignment dependent differential cross sections in co-propagating molecular beams at cold collision energies.

The Journal of chemical physics·2026
Same journal

Non-additive ion effects on the coil-globule equilibrium of a generic polymer in aqueous salt solutions.

The Journal of chemical physics·2026
Same journal

Insights into the unexpected small reduction of the temperature of maximum density of water by lithium chloride addition.

The Journal of chemical physics·2026
Same journal

Optical frequency comb double-resonance spectroscopy of the 9030-9175 cm-1 states of ethylene.

The Journal of chemical physics·2026
Same journal

Time reversal breaking of colloidal particles in cells.

The Journal of chemical physics·2026
查看所有相关文章

相关实验视频

Updated: Feb 5, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

从Poisson-Boltzmann方法通过机器学习实现全原子分子动力学精度.

Ema Slejko1,2, Amaury Coste1,3, Tilen Potisk1,2

  • 1Theory Department, National Institute of Chemistry, SI-1001 Ljubljana, Slovenia.

The Journal of chemical physics
|February 3, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了DIS-PB,这是一个图形神经网络模型,它结合了粗粒水和明确的溶液. 这种方法准确地捕获了短距离和远距离的静电学数据,以较低的计算成本与所有原子分子动态的精度相匹配.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

相关实验视频

Last Updated: Feb 5, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

科学领域:

  • 计算化学是一种计算化学.
  • 生物物理学的生物物理.
  • 分子建模分子建模

背景情况:

  • 全原子分子动力学 (MD) 模拟为生物分子系统提供了高精度,但在计算上昂贵.
  • 隐式模型和粗粒度模型可以降低计算成本,但往往会牺牲准确性.
  • 波桑-博尔兹曼 (PB) 理论有效地模拟了远程静电学,但忽视了关键的短程相互作用.

研究的目的:

  • 开发一种计算方法,将全原子MD的准确性与隐性模型的效率相结合.
  • 为了准确地捕捉生物分子系统中的短距离静电相关性和长距离静电相互作用.
  • 为了降低模拟大型生物分子系统的计算成本,同时保持高精度.

主要方法:

  • 引入了一个图形神经网络 (GNN) Δ学习方法,称为DIS-PB (使用PB潜力作为先验的深隐式解决模型).
  • 在全原子MD和PB计算之间的差异上训练了GNN.
  • 模拟溶解物和盐离子明确使用MD,而水是粗粒的.

主要成果:

  • DIS-PB成功地捕获了短距离静电相关性和长距离静电相互作用尾巴.
  • 在1mol/L盐溶液中使用DIS-PB对DNA分子进行模拟,以高保真度重现结构性质.
  • 经GNN校正的PB方法实现了与全原子MD可比的准确性,但计算成本显著降低.

结论:

  • DIS-PB提供了一种计算效率高且准确的方法来建模生物分子系统.
  • 这种方法有效地弥合了全原子模拟和传统隐式溶剂模型之间的准确差距.
  • 经GNN校正的PB方法对研究复杂的生物分子系统,包括DNA-蛋白相互作用,显示出前途.