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相关概念视频

Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

10.8K
The free energy change for a process taking place with reactants and products present under nonstandard conditions (pressures other than 1 bar; concentrations other than 1 M) is related to the standard free energy change according to this equation:
 
where R is the gas constant (8.314 J/K·mol), T is the absolute temperature in kelvin, and Q is the reaction quotient. This equation may be used to predict the spontaneity of a process under any given set of conditions.
Reaction Quotient...
10.8K
Potential-Energy Criterion for Equilibrium01:16

Potential-Energy Criterion for Equilibrium

503
Potential energy or potential function plays an essential role in determining the stability of a mechanical system. If a system is subjected to both gravitational and elastic forces, the potential function of the system can be expressed as the algebraic sum of gravitational and elastic potential energy. If the system is in equilibrium and is displaced by a small amount, then the work done on the system equals the negative of the change in the system's potential energy from the initial to...
503
Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

20.6K
The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
20.6K
Thermodynamic Potentials01:26

Thermodynamic Potentials

758
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...
758
Free Energy01:21

Free Energy

47.7K
Free energy—abbreviated as G for the scientist Gibbs who discovered it—is a measurement of useful energy that can be extracted from a reaction to do work. It is the energy in a chemical reaction that is available after entropy is accounted for. Reactions that take in energy are considered endergonic and reactions that release energy are exergonic. Plants carry out endergonic reactions by taking in sunlight and carbon dioxide to produce glucose and oxygen. Animals, in turn, break...
47.7K
Free Energy and Equilibrium00:55

Free Energy and Equilibrium

6.0K
The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔG is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
The reaction quotient, Q, is a convenient measure of the...
6.0K

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Updated: May 26, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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学习神经自由能量函数与对相关性匹配的学习神经自由能量函数.

Jacobus Dijkman1,2, Marjolein Dijkstra3, René van Roij4

  • 1University of Amsterdam, Van 't Hoff Institute for Molecular Sciences, The Netherlands.

Physical review letters
|February 21, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一个神经网络来近似赫尔姆霍尔茨自由能量函数,这对于密度函数理论至关重要. 这种方法只使用半径分布函数准确预测系统行为,简化了复杂的模拟.

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科学领域:

  • 计算物理学的计算物理.
  • 统计力学就是统计力学.
  • 材料科学是一种材料科学.

背景情况:

  • 经典密度函数理论依赖于内在的赫尔姆霍尔茨自由能量函数,这通常是3D系统的近似.
  • 准确的自由能量函数对于预测材料属性和相位行为至关重要.

研究的目的:

  • 开发一种用于学习赫尔姆霍尔茨自由能量函数的精确神经网络近似的新方法.
  • 为了规避需要对异质密度配置文件进行计算昂贵的抽样.

主要方法:

  • 一个神经网络被专门训练在一个辐射分布函数的数据集上.
  • 该方法应用于具有平面对称性的超临界列纳德-斯系统.

主要成果:

  • 学习的神经自由能量功能准确地预测了不均的密度配置文件.
  • 预测在来自模拟的各种复杂外部潜力下得到验证.

结论:

  • 这种数据驱动的方法为近似的自由能量函数提供了一个计算效率高的替代方案.
  • 该方法显示了在复杂系统中推进经典密度函数理论应用的前景.