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相关概念视频

Entropy and Solvation02:05

Entropy and Solvation

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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

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In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
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Thermodynamic Potentials01:26

Thermodynamic Potentials

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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...
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Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

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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...
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Intermolecular Forces03:13

Intermolecular Forces

58.0K
Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...
58.0K
Intermolecular Forces in Solutions02:28

Intermolecular Forces in Solutions

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The formation of a solution is an example of a spontaneous process, a process that occurs under specified conditions without energy from some external source.
When the strengths of the intermolecular forces of attraction between solute and solvent species in a solution are no different than those present in the separated components, the solution is formed with no accompanying energy change. Such a solution is called an ideal solution. A mixture of ideal gases (or gases such as helium and argon,...
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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在明确溶剂中使用机器学习的原子间电位的激发状态非adiabatic动态.

Maximilian X Tiefenbacher1,2, Brigitta Bachmair1,2,3, Cheng Giuseppe Chen3,4

  • 1Research Platform on Accelerating Photoreaction Discovery (ViRAPID), University of Vienna Währinger Straße 17 1090 Vienna Austria leticia.gonzalez@univie.ac.at.

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此摘要是机器生成的。

机器学习潜力 (ML/MM) 通过取代昂贵的量子力学/分子力学 (QM/MM) 计算来加速非adiabatic兴奋状态模拟. 这种方法准确地模拟了明确环境中的光诱导过程,例如水中的.

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科学领域:

  • 计算化学的计算化学
  • 摄影化学的使用.
  • 机器学习在科学中的应用

背景情况:

  • 非adiabatic模拟对于理解复杂环境中的光诱导过程至关重要.
  • 传统的量子力学/分子力学 (QM/MM) 方法对于激发状态动力学来说在计算上是昂贵的.
  • 高计算成本限制了QM/MM在轨道表面跳跃方法中的应用.

研究的目的:

  • 为非adiabatic兴奋状态模拟开发一种计算效率高的机器学习 (ML) 方法.
  • 用机器学习的原子间电位 (ML/MM) 取代传统的QM/MM静电嵌入.
  • 验证ML/MM方法对光诱导过程建模的准确性和适用性.

主要方法:

  • 利用FieldSchNet,一种机器学习的原子间潜力,将电场效应纳入电子状态.
  • 实施了ML/MM方法作为QM/MM静电嵌入在非adiabatic兴奋状态轨迹的替代品.
  • 应用了ML/MM方法来模拟水中的的兴奋状态动态,考虑了五个合单体状态.

主要成果:

  • ML/MM模型成功地复制了在QM/MM表面跳跃模拟中观察到的电子动力学和结构重排.
  • 足够精准的训练数据对于ML/MM模型的准确性至关重要.
  • 确定了可靠的性能指标,用于验证ML/MM模型的准确性和可解释性.

结论:

  • 开发的ML/MM方法为非adiabatic兴奋状态模拟提供了QM/MM的计算可行的替代方案.
  • 这种方法可以在明确的分子环境中对光诱导过程进行更广泛的研究.
  • 经过验证的ML/MM模型为激发电子状态的动态提供了可靠的见解.