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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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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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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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Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.
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相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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对于学习和理解分子轨道的笛卡尔等值表示.

Daniel S King1, Daniel Grzenda2, Ray Zhu3

  • 1Department of Chemistry, University of Chicago, Chicago, IL 60637.

Proceedings of the National Academy of Sciences of the United States of America
|November 21, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了卡特西亚等价轨道网络 (CEONET),用于在深度学习中表示分子轨道. CEONET准确地预测轨道能量和特征,帮助电子结构理论和活跃空间选择.

关键词:
化学反应是化学反应.电子结构 电子结构机器学习是机器学习.分子轨道是分子轨道.

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科学领域:

  • 计算化学计算化学
  • 量子化学 是一个量子化学.
  • 机器学习 机器学习

背景情况:

  • 轨道属性对于理解化学反应和兴奋状态行为至关重要.
  • 当前的深度学习模型与几何学和哈密尔顿学相比,缺乏对分子轨道的强有力的表示.

研究的目的:

  • 开发和应用高级等价深度学习架构用于分子轨道表示.
  • 从计算化学数据对轨道赋予全球标签,包括能量和表征.

主要方法:

  • 应用最先进的等价深度学习架构.
  • 开发了笛卡尔等价轨道网络 (CEONET) 来表现分子轨道系数.
  • 利用基于图形的机器学习,具有等价节点特征.

主要成果:

  • CEONET准确地预测了轨道能量和等定量轨道标签.
  • CEONET表示为定性轨道特征 (例如,结合/反结合) 提供了一个可解释的潜空间.
  • 证明了模型通过轨道推断多引用特征的能力.

结论:

  • CEONET提供了一种强大的新表示方式,用于将深度学习与电子结构理论相结合.
  • 该网络促进了先进的电子结构方法的自动化和解释.
  • CEONET的功能为计算机化学中的机器学习活跃空间选择铺平了道路.