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相关概念视频

The Van der Waals Equation01:26

The Van der Waals Equation

92
The ideal gas law is based on two simplifying assumptions: first, that there are no intermolecular attractions between gas molecules, and second, that the volume occupied by the molecules themselves is negligible compared with the volume of the container. However, these assumptions don't hold up under all conditions - specifically, at high pressures and low temperatures, as gas tends to deviate from ideal gas behavior.The van der Waals equation is an enhanced version of the ideal gas law,...
92
Van der Waals Interactions01:24

Van der Waals Interactions

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Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
72.9K
Van der Waals Equation01:10

Van der Waals Equation

6.8K
The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
First, the attractive forces between molecules, which are stronger at higher densities and reduce the pressure, are considered by adding to the pressure a term equal to the square of the molar density multiplied by a positive coefficient a. Second, the volume...
6.8K
Network Covalent Solids02:18

Network Covalent Solids

16.4K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.4K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.8K
VSEPR Theory for Determination of Electron Pair Geometries
46.8K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

14.6K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
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相关实验视频

Updated: Mar 16, 2026

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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机器学习 原子间潜力 实现 CCSD ((T) 准确性 对于具有扩展共价网络和范德瓦尔斯相互作用的系统.

Yuji Ikeda1, Axel Forslund1,2, Pranav Kumar1

  • 1Institute for Materials Science, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany.

Journal of chemical theory and computation
|March 3, 2026
PubMed
概括

我们开发了一种新方法来训练机器学习的原子间潜力 (MLIPs),用于像共价有机框架 (COFs) 这样的大型复杂材料. 这种方法可以实现模拟的高精度,包括范德瓦尔斯相互作用,从而能够对材料性质进行详细分析.

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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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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: Mar 16, 2026

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科学领域:

  • 计算材料科学 计算材料科学
  • 量子化学是一种量子化学.
  • 机器学习是机器学习.

背景情况:

  • 机器学习的原子间潜力 (MLIP) 提供了准确的,具有成本效益的原子模拟.
  • CCSD (T) 计算提供了高精度,但在计算上昂贵,特别是在周期系统中.
  • 现有的MLIP与扩展的共价网络和范德瓦尔斯 (vdW) 相互作用作斗争.

研究的目的:

  • 开发一种用于训练MLIP的方法,用于用于扩展共价网络的CCSD (T) 精度.
  • 为了使具有显著VDW相互作用的系统能够进行准确的大规模原子模拟.
  • 应用开发的MLIP来分析共价有机框架 (COF) 的结构和特性.

主要方法:

  • 使用 Δ-学习方法与分散校正的紧密结合基线.
  • 训练有素的MLIP对CCSD和基线之间的能源差异进行了培训.
  • 在训练组中内置VDW-bound多元仪,以捕捉分散相互作用.
  • 结合VDW感知紧密结合与本地MLIP,以获得CCSD (T) 级准确度.

主要成果:

  • 在训练和测试套件上实现的平方根平均能量误差低于0.4 meV/原子.
  • 精确地复制了电子总原子化能量,键长,振动频率和分子间相互作用.
  • 成功地将该方法应用于近二维COF,分析其结构,层间结合和CCSD (T) 准确度的吸收.

结论:

  • 开发的方法提供了一个实用的途径,用于大规模的原子模拟扩展的共价网络与vdW相互作用.
  • 这种方法实现了化学精度,显著提升了计算材料科学的能力.
  • 能够对像COF这样的复杂材料进行深入分析,这些材料以前受到计算限制的限制.