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

Phase Transitions: Sublimation and Deposition02:33

Phase Transitions: Sublimation and Deposition

19.7K
Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...
19.7K
Chemical Equilibria: Systematic Approach to Equilibrium Calculations01:21

Chemical Equilibria: Systematic Approach to Equilibrium Calculations

1.4K
Equilibrium calculations for systems involving multiple equilibria are often complex. For example, to calculate the solubility of a sparingly soluble salt in an aqueous solution in the presence of a common ion, one must consider all the equilibria in this solution. Calculations for these systems can be complicated and tedious, so a systematic approach with a series of steps is often helpful. The process is detailed below.
The first step is to identify all the chemical reactions involved, The...
1.4K
Multimachine Stability01:25

Multimachine Stability

548
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
548
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

250
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
250
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

338
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
338
Dynamic Equilibrium02:20

Dynamic Equilibrium

61.8K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
61.8K

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

Entropy, Free Energy, and a Generalized Order Parameter for Liquid Crystal Phases of Chiral and Achiral Rods.

Journal of chemical theory and computation·2026
Same author

Accuracy of Several Cubic Equations of State for Predicting Vapor Pressures Using Critical Properties from Experimental Constants, PR+COSMOSAC-Based Constants, and Full PR+COSMOSAC Calculations.

The journal of physical chemistry. A·2026
Same author

Thermodynamic origins of the interfacial-bulk solubility trade-off for CO<sub>2</sub> in ionic liquids: a molecular dynamics simulation study.

Physical chemistry chemical physics : PCCP·2026
Same author

TRACE: A Topological Algorithm for Detecting Additive-Coordinated Hydrate Cages.

Journal of chemical theory and computation·2025
Same author

The Surprising Role of Urea in Promoting CO<sub>2</sub> Hydrate Formation: Enhanced Molecular Diffusivity via Weakening of the Hydrogen-Bond Network.

The journal of physical chemistry. B·2025
Same author

Effect of methane concentration on the formation pathways of methane hydrate near hexagonal ice surfaces.

Physical chemistry chemical physics : PCCP·2025
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
Same journal

Derisking Affinity Optimization for Macrocycles and Cyclic Peptides: High-Precision Free Energy Simulations across Five Diverse Targets.

Journal of chemical information and modeling·2026
Same journal

An End-User Audit of Reproducibility, Data Leakage, and Overfitting of the Top-Ranked ADMET Prediction Models in TDC Leaderboards.

Journal of chemical information and modeling·2026
Same journal

PFASGroups: An Open-Source Framework for Automated Identification, Structural Classification, and Prioritization of Per- and Polyfluoroalkyl Substances.

Journal of chemical information and modeling·2026
Same journal

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Jan 17, 2026

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers
12:37

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers

Published on: September 4, 2015

12.9K

物理嵌入式机器学习模型用于在多元件系统中预测相位平衡.

Yue Yang1, Shiang-Tai Lin1

  • 1Department of Chemical Engineering, National Taiwan University, Taipei 106319, Taiwan.

Journal of chemical information and modeling
|September 22, 2025
PubMed
概括
此摘要是机器生成的。

我们介绍了热力学嵌入式细分活动系数神经网络 (TeNNet-SAC) 模型. 这种机器学习框架只使用分子SMILES字符串准确预测液体混合物的活性系数.

更多相关视频

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy
10:08

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Published on: October 24, 2017

9.6K
High-pressure Sapphire Cell for Phase Equilibria Measurements of CO2/Organic/Water Systems
05:46

High-pressure Sapphire Cell for Phase Equilibria Measurements of CO2/Organic/Water Systems

Published on: January 24, 2014

13.9K

相关实验视频

Last Updated: Jan 17, 2026

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers
12:37

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers

Published on: September 4, 2015

12.9K
Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy
10:08

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Published on: October 24, 2017

9.6K
High-pressure Sapphire Cell for Phase Equilibria Measurements of CO2/Organic/Water Systems
05:46

High-pressure Sapphire Cell for Phase Equilibria Measurements of CO2/Organic/Water Systems

Published on: January 24, 2014

13.9K

科学领域:

  • 物理化学 物理化学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 预测液体混合物中的活性系数对于化学过程设计至关重要.
  • 像COSMO-SAC这样的现有模型依赖于复杂的量子化学计算.
  • 需要使用更简单的分子表示方法来实现准确和可扩展的方法.

研究的目的:

  • 开发一个新的机器学习框架,TeNNet-SAC,用于预测活动系数.
  • 仅使用SMILES表示来输入,简化数据要求.
  • 在预测中实现高精度和热力学一致性.

主要方法:

  • TeNNet-SAC集成了一个 σ-profile预测器,一个几何预测器和一个 Γ预测器.
  • 预测者接受了量子溶解计算和合成数据的培训.
  • 该模型与实验活动系数数据进行了微调,以提高准确性.

主要成果:

  • 基本的TeNNet-SAC模型显示的准确性与COSMO-SAC相美.
  • 精心调整的TeNNet-SAC模型始终表现优于COSMO-SAC.
  • 该模型展示了对多组分混合物的自然概括.

结论:

  • TeNNet-SAC为活动系数预测提供了一个强大的,可扩展的替代方案.
  • 该框架利用机器学习进行高效的化学性质估计.
  • 该方法满足热力学一致性,确保可靠的预测.