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相关概念视频

Electronic Structure of Atoms02:28

Electronic Structure of Atoms

21.5K

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...
21.5K
Colors and Magnetism03:02

Colors and Magnetism

11.9K
Color in Coordination Complexes
When atoms or molecules absorb light at the proper frequency, their electrons are excited to higher-energy orbitals. For many main group atoms and molecules, the absorbed photons are in the ultraviolet range of the electromagnetic spectrum, which cannot be detected by the human eye. For coordination compounds, the energy difference between the d orbitals often allows photons in the visible range to be absorbed and emitted, which is seen as colors by the human...
11.9K
Ferromagnetism01:31

Ferromagnetism

2.4K
Materials like iron, nickel, and cobalt consist of magnetic domains, within which the magnetic dipoles are arranged parallel to each other. The magnetic dipoles are rigidly aligned in the same direction within a domain by quantum mechanical coupling among the atoms. This coupling is so strong that even thermal agitation at room temperature cannot break it. The result is that each domain has a net dipole moment. However, some materials have weaker coupling, and are ferromagnetic at lower...
2.4K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.1K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.1K
Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

1.0K
NMR-active nuclei have energy levels called 'spin states' that are associated with the orientations of their nuclear magnetic moments. In the absence of a magnetic field, the nuclear magnetic moments are randomly oriented, and the spin states are degenerate. When an external magnetic field is applied, the spin states have only 2 + 1 orientations available to them. A proton with = ½ has two available orientations. Similarly, for a quadrupolar nucleus with a nuclear spin value of...
1.0K
The Pauli Exclusion Principle03:06

The Pauli Exclusion Principle

39.1K
The arrangement of electrons in the orbitals of an atom is called its electron configuration. We describe an electron configuration with a symbol that contains three pieces of information:
39.1K

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Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
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通过使用机器学习从电子结构中对磁性顺序进行分类.

Yerin Jang1, Choong H Kim2,3, Ara Go4

  • 1Department of Physics, Chonnam National University, Gwangju, 61186, Korea.

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概括

机器学习现在可以从激发光谱中识别磁态,克服中子散射极限. 这种方法使用光谱数据和激发能量准确地分类反铁磁顺序.

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科学领域:

  • 凝聚物质物理学 凝聚物质物理学
  • 材料科学 材料科学 材料科学
  • 计算物理 计算物理

背景情况:

  • 识别磁性状态对于材料应用至关重要.
  • 中子散射实验对直接磁性状态识别有局限性.
  • 开发用于磁性状态识别的替代方法至关重要.

研究的目的:

  • 开发一种机器学习方法,从自旋集成激发光谱中识别磁态.
  • 使用决策树算法来分类反铁磁序列.
  • 探索光谱数据和激发能作为机器学习特征的有效性.

主要方法:

  • 在Wannier哈密尔顿式上使用Hartree-Fock平均场计算生成了一个数据集.
  • 从BaOsO的第一原则计算中提取的光谱数据[公式:见文本].
  • 训练有素的决策树机器学习模型使用局部状态密度,动量解析状态密度和激发能量.

主要成果:

  • 机器学习模型成功地从光谱数据中识别了反铁磁顺序.
  • 扩大方法显著影响了模型性能.
  • 将激发能作为一个特征,提高了分类准确性,即使对于各种测试样本.

结论:

  • 机器学习为识别磁态提供了一个可行的替代方案.
  • 刺激能量是改善磁性状态分类的有价值特征.
  • 开发的方法证明了不同数据生成方法的稳定性.