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相关概念视频

Electronic Structure of Atoms02:28

Electronic Structure of Atoms

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An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...
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Electron Orbital Model01:18

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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.
The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...
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What is Variation?01:14

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
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This lesson delves into the geometry of a radical, which is influenced by the electronic structure of the molecule. The principle is similar to that of a lone pair, where the unpaired electron influences the geometry at the radical center.
Accordingly, the structure of a trivalent radical lies between the geometries of carbocations and carbanions. An sp2-hybridized carbocation is trigonal planar, while an sp3-hybridized carbanion is trigonal pyramidal. Here, the difference in geometry is...
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Simplified Synchronous Machine Model01:30

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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通过密度矩阵优化电子结构的变量机器学习模型.

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  • 1Zhejiang University, School of Physics, Hangzhou 310027, China.

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概括
此摘要是机器生成的。

我们开发了一种新的机器学习方法来解决密度函数理论中的Kohn-Sham方程. 这种方法使用神经网络直接优化基本状态,绕过了用于更快电子结构计算的传统方法.

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科学领域:

  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学
  • 量子物理学 量子物理学 是一种量子物理学.

背景情况:

  • 密度函数理论 (DFT) 对于预测材料性能至关重要.
  • 解决Kohn-Sham方程是计算密集的.
  • 目前的方法,如自一致场 (SCF) 方法,对于大型系统来说可能很慢.

研究的目的:

  • 开发一种新的,高效的机器学习方法来解决Kohn-Sham方程.
  • 绕过电子结构计算中的传统计算瓶.
  • 为了能够准确地预测分子和扩展系统的基本状态属性.

主要方法:

  • 通过密度矩阵将机器学习与直接变量能量优化相结合.
  • 使用等价神经网络来预测物理约束密度矩阵.
  • 绕过哈密尔顿矩阵对角化和传统的SCF方法.
  • 将训练集构建集成到模型训练过程中.

主要成果:

  • 在预测基态属性方面达到高准确度.
  • 在能源最小化方面表现出稳定性和效率.
  • 成功应用于分子和扩展系统.
  • 为电子结构优化建立了一个新的机器学习范式.

结论:

  • 新型ML方法为电子结构计算的传统方法提供了强大的替代方案.
  • 这种方法为更高效,更准确的大规模量子模拟铺平了道路.
  • 直接密度矩阵优化显示了对推进计算材料科学的重大前景.