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相关概念视频

Molecular Orbital Theory II03:51

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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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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.
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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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机器学习 精确的交换相关潜力 减少密度函数理论中的移位错误

Yuan Zhuang1,2, Yonghao Gu2,1, Beini Zhang2

  • 1Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR 999077, China.

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概括
此摘要是机器生成的。

机器学习通过减少密度函数理论中的移位错误来准确预测分子性质. 我们的新型深度神经网络方法克服了传统和当前机器学习的局限性.

关键词:
深度神经网络移位错误;交换相关性潜力密度函数理论电双极时刻机器学习

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科学领域:

  • 计算化学
  • 材料科学
  • 量子力学

背景情况:

  • 密度函数理论 (DFT) 是一种用于电子结构计算的强大的量子力学方法.
  • 移位错误是DFT的一个重要限制,特别是在拉伸的分子系统中.
  • 现有的传统和机器学习的函数经常无法准确地描述这些系统.

研究的目的:

  • 开发一种机器学习方法,用于在DFT中生成准确的交换相关潜力.
  • 解决和减少电子结构计算中的移位错误.
  • 提高对具有挑战性的系统的分子性质的预测.

主要方法:

  • 用一个深度神经网络来解决Kohn-Sham方程.
  • 开发了一种新的机器学习功能,
  • 在传统方法失败的分子系统上测试了方法.

主要成果:

  • 训练有素的功能精确地捕捉了伸展的分子系统的解离极限.
  • 与电子密度和电二极矩的CCSD参考数据取得了很好的一致性.
  • 证明了原子力的准确预测,克服了现有方法的局限性.

结论:

  • 拟议的机器学习方法有效地减少了DFT的迁移错误.
  • 这种方法提供了一个可靠的工具,可以准确地预测各种化学物种的分子性质.
  • 为计算化学和材料科学应用提供了有前途的进步.