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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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Plane Electromagnetic Waves II01:29

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Consider a plane wavefront traveling in position x-direction with a constant speed. This wavefront can be utilized to obtain the relationship between electric and magnetic fields with the help of Faraday's law.
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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Valence Bond Theory and Hybridized Orbitals02:38

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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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Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

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Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...
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The existence of combined electric and magnetic fields that propagate through space as electromagnetic (EM) waves is the most significant prediction of Maxwell's equations. As Maxwell's equations hold in free space, the predicted electromagnetic waves do not require a medium for their propagation. An EM wave comprises an electric field, defined as the force per charge on a stationary charge, and a magnetic field, which is the force per charge on a moving charge.
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相关实验视频

Updated: Jun 11, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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将深度学习电子结构计算推广到平面波基础.

Xiaoxun Gong1,2,3, Steven G Louie4,5, Wenhui Duan6,7,8

  • 1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China.

Nature computational science
|October 3, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新的方法来表示密度函数理论 (DFT) 哈密尔顿人使用深度神经网络. 这种方法弥合了原子轨道 (AO) 和平面波 (PW) 基之间的差距,使更准确的电子结构计算成为可能.

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相关实验视频

Last Updated: Jun 11, 2025

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 量子化学 是一个量子化学.
  • 科学中的人工智能.

背景情况:

  • 深度神经网络 (DNN) 通过学习密度函数理论 (DFT) 显示了电子结构计算的潜力.
  • 现有的DNN仅限于原子轨道 (AO) 基础,不包括广泛使用的平面波 (PW) 基础.

研究的目的:

  • 开发一种使DNN能够利用平面波 (PW) 基础的DFT结果进行电子结构计算的方法.
  • 弥合基于AO的深度学习和基于PW的DFT方法之间的差距.

主要方法:

  • 提出了一个真实空间重建方法,直接从PW DFT结果计算AO哈密尔顿矩阵.
  • 与传统的基于投影的技术相比,证明了重建方法的效率和准确性.

主要成果:

  • 重建方法比传统的投影方法快得多 (数量级).
  • 重建的哈密尔顿矩阵准确地复制使用PW基础计算的电子结构.
  • 成功地将PW方法的优点 (准确性,灵活性,适用性) 整合到深度学习电子结构方法中.

结论:

  • 开发的方法克服了以前用于DFT的DNN的基本限制.
  • 允许使用PW DFT创建大规模的高保真训练数据集.
  • 为电子结构开发更精确,更广泛的深度学习模型铺平了道路.