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相关概念视频

Maxwell's Thermodynamic Relations01:23

Maxwell's Thermodynamic Relations

2.4K
Maxwell's thermodynamic relations are very useful in solving problems in thermodynamics. Each of Maxwell's relations relates a partial differential between quantities that can be hard to measure experimentally to a partial differential between quantities that can be easily measured. These relations are a set of equations derivable from the symmetry of the second derivatives and the thermodynamic potentials.
All thermodynamic potentials are exact differentials. Therefore, their second-order...
2.4K
Free Energy01:21

Free Energy

47.6K
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...
47.6K
Thermodynamics: Activity Coefficient01:24

Thermodynamics: Activity Coefficient

1.3K
Activity is the measure of the effective concentration of the species in solution. It can be expressed as the product of the molar concentration of the species and its activity coefficient. The activity coefficient is a dimensionless quantity and depends on the total ionic strength of the solution.
The activity coefficient is a measure of the deviation from ideal behavior. When the ionic strength of the solution is minimal, the activity coefficient of an ionic species is close to unity, making...
1.3K
Applications of the Ideal Gas Law: Molar Mass, Density, and Volume03:43

Applications of the Ideal Gas Law: Molar Mass, Density, and Volume

56.1K
The volume occupied by one mole of a substance is its molar volume. The ideal gas law, PV = nRT,  suggests that the volume of a given quantity of gas and the number of moles in a given volume of gas vary with changes in pressure and temperature. At standard temperature and pressure, or STP (273.15 K and 1 atm), one mole of an ideal gas (regardless of its identity) has a volume of about 22.4 L — this is referred to as the standard molar volume.
56.1K
Thermodynamic Potentials01:26

Thermodynamic Potentials

749
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...
749
Path Between Thermodynamics States01:21

Path Between Thermodynamics States

3.0K
Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
3.0K

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相关实验视频

Updated: May 21, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Published on: April 8, 2020

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提高密度的机器学习功能理论热力学热力学.

Sergei I Simak1,2, Erna K Delczeg-Czirjak3,4, Olle Eriksson3,4

  • 1Department of Physics, Chemistry and Biology (IFM), Linköping University, 581 83, Linköping, Sweden. sersi78@liu.se.

Scientific reports
|May 17, 2025
PubMed
概括
此摘要是机器生成的。

机器学习纠正密度函数理论 (DFT) 错误在合金形成度. 这提高了对三元相稳定的预测,这对于材料科学和高温应用至关重要.

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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相关实验视频

Last Updated: May 21, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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科学领域:

  • 计算材料科学科学 计算材料科学
  • 机器学习在化学中的应用
  • 合金的热力学 合金的热力学

背景情况:

  • 密度函数理论 (DFT) 是预测材料属性的强大工具,但存在能量分辨率错误.
  • 这些错误极大地影响了合金形成度的精度,特别是在复杂的三元系统中.
  • 可靠地预测相位稳定性对于设计适用于苛刻应用的新材料至关重要.

研究的目的:

  • 开发一种机器学习 (ML) 方法,以系统地纠正合金形成度中的 DFT 能量错误.
  • 提高对二元和三元合金和化合物的第一原则计算的预测准确度.
  • 提高有关航空航天和保护涂层的材料相位稳定性预测的可靠性.

主要方法:

  • 训练了一个神经网络模型来预测DFT和实验度之间的差异.
  • 该模型使用了一个包括元素度,原子数和相互作用项在内的特征集.
  • 使用监督学习,严格的数据策划和交叉验证 (LOOCV,k-fold) 来确保模型的稳定性并防止过度拟合.

主要成果:

  • ML模型成功地学会了预测和纠正合金形成度的DFT能量错误.
  • 对Al-Ni-Pd和Al-Ni-Ti系统的应用显示出预测准确性的显著改善.
  • 修正后的预测提供了对三元相稳定性的更可靠的见解.

结论:

  • 提出的ML方法提供了一种可靠的方法,用于纠正合金形成热量中的DFT能量错误.
  • 这显著提高了合金相稳定第一原则预测的可靠性.
  • 修正后的预测对于加速发现用于高温应用的新材料非常有价值.