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相关概念视频

Molecular Orbital Theory II03:51

Molecular Orbital Theory II

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Molecular Orbital Theory I02:35

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Overview of Molecular Orbital Theory
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Valence Bond Theory and Hybridized Orbitals02:38

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According to valence bond theory, a covalent bond results when: (1) an orbital on one atom overlaps an orbital on a second atom, and (2) the single electrons in each orbital combine to form an electron pair. The strength of a covalent bond depends on the extent of overlap of the orbitals involved. Maximum overlap is possible when the orbitals overlap on a direct line between the two nuclei.
A σ bond (single bond in a Lewis structure) is a covalent bond in which the electron density is...
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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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Atomic Orbitals02:44

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An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.
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In an atom, the negatively charged electrons are attracted to the positively charged nucleus. In a multielectron atom, electron-electron repulsions are also observed. The attractive and repulsive forces are dependent on the distance between the particles, as well as the sign and magnitude of the charges on the individual particles. When the charges on the particles are opposite, they attract each other. If both particles have the same charge, they repel each other.
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基于机器学习的稳定和准确的无轨密度功能理论

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概括
此摘要是机器生成的。

机器学习现在为计算分子能量和电子密度提供了准确的密度函数. 这种方法实现了有机分子的化学精度,推进了计算化学.

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科学领域:

  • 计算化学
  • 量子力学
  • 材料科学

背景情况:

  • 霍恩伯格-科恩定理为密度函数理论 (DFT) 奠定了理论基础.
  • 对精确的能量函数的准确近似仍然是DFT的一个重大挑战.
  • 现有的功能常常缺乏各种化学应用所需的精度.

研究的目的:

  • 使用机器学习开发经验推导的密度函数.
  • 在能量计算中实现化学准确性和分子的有意义的电子密度.
  • 弥合理论DFT与实践计算化学之间的差距.

主要方法:

  • 使用旋转等价的原子学机器学习.
  • 在QM9有机分子数据集上训练了一个模型.
  • 增加了来自扰动潜力的电子密度的训练数据.

主要成果:

  • 开发了STRUCTURES25密度函数
  • 获得的能量与Kohn-Sham计算相对准确.
  • 获得有机分子的收和有意义的电子密度.

结论:

  • 机器学习提供了学习精确密度函数的可行途径.
  • 这项工作证明了实现霍恩伯格-科恩愿景的实际进展.
  • 能够对大型分子系统进行更高效,更准确的电子结构计算.