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 Reaction Outcomes02:24

Predicting Reaction Outcomes

10.0K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
10.0K
Energy Diagrams, Transition States, and Intermediates02:13

Energy Diagrams, Transition States, and Intermediates

20.0K
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...
20.0K
Energy Transfer in Chemical Reactions01:16

Energy Transfer in Chemical Reactions

10.6K
Chemical reactions require sufficient energy to cause the matter to collide with enough precision and force that old chemical bonds can be broken and new ones formed. In general, kinetic energy is the form of energy powering any type of matter in motion. Imagine a person building a brick wall. The energy it takes to lift and place one brick on top of another is the kinetic energy—the energy matter possesses because of its motion. Once the wall is in place, it stores potential energy.
10.6K
Support Reactions in Three Dimensions01:27

Support Reactions in Three Dimensions

1.6K
Support reactions in three dimensions help maintain the stability and equilibrium of various structures and systems. These reactions prevent the system from translating and rotating, ensuring the design can withstand external forces and perform its intended function efficiently and safely. Some of the supports providing support reactions in three dimensions are discussed below:
Ball and Socket Joint is one of the supports allowing free rotation about any axis. This freedom of rotation is...
1.6K
Arrhenius Plots02:34

Arrhenius Plots

46.6K
The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
The Arrhenius equation can be used...
46.6K
The Nernst Equation02:59

The Nernst Equation

46.5K
Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
46.5K

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

Deep-Penetrating Transdermal Lipopeptide Liposomes for Sustained IL-17 Inhibition and Prevention of Psoriatic Recurrence.

Journal of the American Chemical Society·2026
Same author

AmberTorchPB: A Unified Framework for Poisson-Boltzmann-Based Reaction Field Energy Calculation via Tensor Computation.

Journal of chemical theory and computation·2026
Same author

Single-cell omics arena: evaluation of large language models for automatic cell-type annotations on single-cell omics data via RNA-seq bridging.

Briefings in bioinformatics·2025
Same author

DEGAUSS: A Novel Softcore Force Field Using Double Exponential van der Waals and Gaussian Charge for Molecular Dynamics Simulations I: Theory and Validation.

Journal of chemical theory and computation·2025
Same author

Optimization of Lennard-Jones Parameters for Induced Dipole Polarizable Gaussian Multipole Force Field.

The journal of physical chemistry. B·2025
Same author

Biomolecular Condensates as Emerging Biomaterials: Functional Mechanisms and Advances in Computational and Experimental Approaches.

Advanced materials (Deerfield Beach, Fla.)·2025
Same journal

Improving PCM in Protic Media: Markov State Models for TD-DFT Calculations.

Journal of chemical theory and computation·2026
Same journal

Efficient Coupled-Cluster Python Frameworks for Next-Generation GPUs: A Comparative Study of CuPy and PyTorch on the Hopper and Grace Hopper Architecture.

Journal of chemical theory and computation·2026
Same journal

Extending the MARTINI 3 Coarse-Grained Force Field to Polypeptoids.

Journal of chemical theory and computation·2026
Same journal

Statistical Mechanics of Density- and Temperature-Dependent Potentials: Application to Condensed Phases within GenDPDE.

Journal of chemical theory and computation·2026
Same journal

BFEE-Docking: A User-Friendly and Customizable End-to-End Tool from High-Throughput Virtual Screening to Binding Free-Energy Calculations.

Journal of chemical theory and computation·2026
Same journal

On-the-Fly Trajectory Simulation of Two-Pulse, Three-Pulse, and Higher-Order Pump-Probe Signals.

Journal of chemical theory and computation·2026
查看所有相关文章

相关实验视频

Updated: Jan 16, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K

使用数据驱动的几何图形神经网络对反应场能量进行端到端建模.

Yongxian Wu1, Qiang Zhu1, Ray Luo1

  • 1Department of Chemical and Biomolecular Engineering, Molecular Biology and Biochemistry, Materials Science and Engineering, and Biomedical Engineering, University of California, Irvine, California 92697, United States.

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

新的图形神经网络模型PBGNN可以准确地预测生物分子中的静电相互作用,而无需近似. 这种数据驱动的方法为药物发现和分子建模提供了可扩展和精确的能量计算.

更多相关视频

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K
Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

612

相关实验视频

Last Updated: Jan 16, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K
3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K
Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
05:37

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Published on: August 22, 2025

612

科学领域:

  • 计算化学和分子建模.
  • 生物物理学和结构生物学.
  • 机器学习用于科学应用.

背景情况:

  • 静电相互作用对生物分子结构,动力学和功能至关重要.
  • 波桑-博尔兹曼 (Poisson-Boltzmann) 方程准确地模拟了这些相互作用,但在计算上是密集的.
  • 像一般化出生 (GB) 模型这样的现有近似方法为了效率而牺牲准确性.

研究的目的:

  • 开发一种计算效率高,准确的方法来计算PB静电能.
  • 为了克服传统的PB解决方案和GB近似的局限性.
  • 在药物发现中,为大生物分子和小分子提供精确的静电建模.

主要方法:

  • 开发了PBGNN,一个使用几何图形神经网络的新型端到端框架.
  • 嵌入了原子电荷和传递信息架构的状嵌入.
  • 引入了负担加权平均平方误差 (CMSE) 目标,以稳定培训.

主要成果:

  • 在预测具有线性计算复杂性的PB能量方面,PBGNN实现了高精度.
  • 证明了对生物分子复合体和小分子的可靠和精确的PB自由能量预测.
  • 展示了强大的通用性,可扩展性和药物发现应用的潜力.

结论:

  • PBGNN为静电建模提供了一个可扩展和准确的替代方案,超越了GB近似值.
  • 该框架在各种数据集上的性能突出显示了其在计算化学和药物发现中的实用性.
  • 开源发布的PBGNN促进了对精确静电分析的进一步研究.