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相关概念视频

Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

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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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Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

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sp3d and sp3d 2 Hybridization
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Atomic Orbitals02:44

Atomic Orbitals

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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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Atomic Structure01:17

Atomic Structure

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The Greek philosopher Democritus proposed that everything on Earth is made up of tiny particles called atomos, Greek for "indivisible," from which the modern term "atom" is derived. In the 19th century, John Dalton proposed the atomic theory that is still largely correct today. He put forth five postulates to explain how atoms made up the world around us. (1) All matter is composed of infinitely small particles or atoms. (2) All atoms of a given element are identical to one...
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Valence Bond Theory and Hybridized Orbitals02:38

Valence Bond Theory and Hybridized Orbitals

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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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Atomic Radii and Effective Nuclear Charge03:08

Atomic Radii and Effective Nuclear Charge

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The elements in groups of the periodic table exhibit similar chemical behavior. This similarity occurs because the members of a group have the same number and distribution of electrons in their valence shells.
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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对原子配置交互问题的深度学习方法在大型基础上设置集.

Pavlo Bilous1,2, Adriana Pálffy2,3, Florian Marquardt1,4

  • 1Max Planck Institute for the Science of Light, Staudtstraße 2, 91058 Erlangen, Germany.

Physical review letters
|October 13, 2023
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概括
此摘要是机器生成的。

本研究介绍了一种深度学习方法,以有效地解决原子结构计算中的配置相互作用 (CI) 问题. 这种新的方法使用卷积神经网络来选择相关的配置,使大规模,以前难以解决的问题能够获得准确的结果.

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科学领域:

  • 计算物理 计算物理
  • 量子化学 是一个量子化学.
  • 人工智能的人工智能

背景情况:

  • 高精度的原子结构计算需要精确的电子相关性建模.
  • 对这种建模至关重要的配置交互 (CI) 问题涉及计算上昂贵的多重配置波函数扩展.
  • 现有的方法面临着可扩展性挑战,即使对于日益复杂的超级计算机来说也变得不可行.

研究的目的:

  • 开发一种深度学习方法,用于在大型CI基础集中预先选择相关配置.
  • 用一系列较小,易于管理的计算来取代计算密集的全CI计算.
  • 为了在原子结构计算中高效地实现目标能量精度.

主要方法:

  • 开发了一种使用卷积神经网络 (CNN) 的深度学习方法.
  • CNN反复地管理CI基础集的扩展子集,选择最相关的配置.
  • 这种方法取代了密集的神经网络架构,这些架构被发现不适合量子化学问题.

主要成果:

  • 基于CNN的方法成功执行了强大而准确的CI计算.
  • 该方法在中等规模的基准集上进行了基准测试,与直接CI计算对其准确性进行了验证.
  • 该方法证明了对不可承受的大CI基数集的可行性,在这些基数集中,直接计算是不可能的.

结论:

  • 深度学习,特别是CNN,为原子物理中的大规模配置交互问题提供了可行和高效的解决方案.
  • 这种新的方法显著提高了高精度原子结构计算的可行性.
  • 这种方法为解决计算化学和物理中的更复杂的电子相关性问题铺平了道路.