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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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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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使用基于域的局部对自然轨道的随机阶段近似方法的高效实施.

Yu Hsuan Liang1, Xing Zhang2, Garnet Kin-Lic Chan2

  • 1Department of Chemistry, Columbia University, New York, New York 10027, United States.

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概括
此摘要是机器生成的。

我们开发了一种高效的基于域的局部对自然轨道 (DLPNO) 方法,用于分子系统中的随机相近似 (RPA). 这种方法显著降低了计算成本,同时保持了电子结构计算的高精度.

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科学领域:

  • 量子化学 是一个量子化学.
  • 计算化学的计算化学
  • 理论化学 理论化学

背景情况:

  • 精确计算分子性质对于理解化学反应和设计新材料至关重要.
  • 电子结构计算的传统方法可能在计算上昂贵,限制了它们对更大的系统的应用.

研究的目的:

  • 为分子系统提供基于域的局部对自然轨道 (DLPNO) 框架内直接随机相近似 (RPA) 的高效实现.
  • 为了能够精确计算反应能量和潜在能量表面,并降低计算成本.

主要方法:

  • 使用基于域的局部对自然轨道 (DLPNO) 技术实现直接随机相近似 (RPA).
  • 使用了DLPNO-RPA的宽松,正常和紧密参数设置.
  • 应用该方法来计算大分子的基数集融合结合能.

主要成果:

  • 与正规的RPA相比,DLPNO-RPA在总相关性能量方面达到99.7-99.95%的准确性.
  • 极其精确的计算计算的计算成本大幅降低.
  • 在DLPNO-RPA绑定能量和高级参考数据 (合集群,扩散蒙特卡洛) 之间达成了很好的协议.

结论:

  • DLPNO-RPA方法为分子电子结构计算提供了一个计算效率高,准确的方法.
  • 这一发展促进了基于RPA的方法在分子量子化学中的常规应用.
  • 能够准确地预测大型分子系统的结合能.