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相关概念视频

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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Scanning Electron Microscopy01:07

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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
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Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

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Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
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Magnetic Moment of an Electron01:23

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Electrons revolving around a nucleus are analogous to a circular current carrying loop. This current produces a magnetic dipole moment proportional to the electron's orbital angular momentum. Since the orbital angular momentum is quantized in terms of the reduced Planck's constant, the dipole moment is quantized in the Bohr Magneton. The value of the Bohr magneton is 9.27 x 10-24 Am2. Electrons also have an intrinsic spin angular momentum, and the associated spin magnetic moment is...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Transmission Electron Microscopy01:15

Transmission Electron Microscopy

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In 1931, physicist Ernst Ruska—building on the idea that magnetic fields can direct an electron beam just as lenses can direct a beam of light in an optical microscope—developed the first prototype of the electron microscope. This development led to the development of the field of electron microscopy. In the transmission electron microscope (TEM), electrons are produced by a hot tungsten element and accelerated by a potential difference in an electron gun, which gives them up to 400...
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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用于机器学习的高效采样 电子密度及其在真实空间中的反应

Chaoqiang Feng1, Yaolong Zhang2, Bin Jiang3,4

  • 1Hefei National Research Center for Physical Sciences at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.

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此摘要是机器生成的。

本研究引入了一种高效的机器学习模型,用于使用一种新的网点采样策略来预测电子密度. 该模型准确地预测了电子密度及其对电场的反应,训练点较少.

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科学领域:

  • 计算化学是一种计算化学.
  • 材料科学 是一种材料科学.
  • 机器学习是机器学习.

背景情况:

  • 电子密度对于确定基态电子性质至关重要.
  • 现有的电子密度机器学习 (ML) 模型需要广泛的基础函数或网点.
  • 为真实空间电子密度开发高效的ML模型具有挑战性.

研究的目的:

  • 为电子密度及其对电场的反应开发一种高效的基于现实空间网格的ML模型.
  • 为了减少精确电子密度预测所需的培训点的数量.
  • 为了分析各种系统中的电荷分布和电场效应.

主要方法:

  • 实施了一种高效的网点采样策略,结合了有针对性的采样和特征选.
  • 整合了采样策略与场诱导的递归嵌入式原子神经网络模型.
  • 将ML模型应用于QM9分子数据,H2O/Pt111) 接口,Au100) 电极和Au纳米粒子.

主要成果:

  • 使用比以前的模型少得多的训练点,实现了对电子密度的比较准确的预测.
  • 成功预测了电子密度及其对不同系统电场的反应.
  • 实现了准确的部分电荷分割和在接口系统中分析电荷变化.

结论:

  • 提出的高效网点采样策略显著增强了电子密度的ML模型.
  • 开发的ML模型准确地预测了电子特性和对电场的反应.
  • 这种方法为研究电子密度和电荷动态提供了一种计算效率高的方法.