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相关概念视频

Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

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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...
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Atomic Nuclei: Nuclear Magnetic Moment00:59

Atomic Nuclei: Nuclear Magnetic Moment

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All atomic nuclei are positively charged. When they have a nonzero spin, they behave like rotating charges. As a consequence of their charge and spin, these nuclei generate a magnetic field (B). This, in turn, gives rise to a magnetic moment (μ), which is randomly oriented in the absence of an external magnetic field. When an external magnetic field (B0) is applied, the magnetic moment vectors can align with the field or against it in 2 + 1 orientations. A hydrogen nucleus, which is just a...
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Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

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Near absolute zero temperatures, in the presence of a magnetic field, the majority of nuclei prefer the lower energy spin-up state to the higher energy spin-down state. As temperatures increase, the energy from thermal collisions distributes the spins more equally between the two states. The Boltzmann distribution equation gives the ratio of the number of spins predicted in the spin −½ (N−) and spin +½ (N+) states.
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Ferromagnetism01:31

Ferromagnetism

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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...
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Diamagnetism01:26

Diamagnetism

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Materials consisting of paired electrons have zero net magnetic moments. However, when these materials are placed under an external magnetic field, the moments opposite to the field are induced. Such materials are called diamagnets. Diamagnetism is the response of the diamagnets when placed in an external magnetic field.
Diamagnetism was discovered by Anton Brugmans in 1778 when he observed that bismuth gets repelled by magnetic fields, thus theorizing that diamagnets get repelled by magnets....
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Magnetic Vector Potential01:15

Magnetic Vector Potential

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In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
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相关实验视频

Updated: Sep 17, 2025

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
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以旋转信息为基础的通用图形神经网络,用于模拟磁性排序.

Wenbin Xu1,2, Rohan Yuri Sanspeur3, Adeesh Kolluru3

  • 1National Energy Research Scientific Computing Center, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America
|July 1, 2025
PubMed
概括

我们开发了一个旋转信息图形神经网络,以加速磁性材料的发现. 这一框架改善了初始磁矩预测,加快了计算速度,并实现了准确的基态顺序确定.

关键词:
以数据为中心的AI.磁性材料是一种磁性材料.磁性订购是指磁性订购.uMLIPLIP 在线阅读

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算物理 计算物理
  • 机器学习 机器学习

背景情况:

  • 使用密度功能理论 (DFT) 发现磁性材料在计算上是昂贵的.
  • 目前的通用机器学习原子间潜力 (uMLIP) 缺乏磁性排序预测能力.
  • 现有的方法在确定基态磁性配置的高计算成本下扎.

研究的目的:

  • 开发一种机器学习框架,以高效预测磁顺序.
  • 扩展通用机器学习原子间潜能 (uMLIPs) 的功能,包括磁性特性.
  • 加速对新型磁性材料的选和发现.

主要方法:

  • 开发了一个数据效率高,自旋信息化的图形神经网络框架.
  • 该框架包含旋转自由度,并保持物理对称性.
  • 为数据集增强,实施了闭环异常检测方法.

主要成果:

  • 该框架通过提供更好的磁矩初始猜测,显著加快密度函数理论 (DFT) 的计算速度.
  • 在散装材料中精确地确定了地面状态的磁性排序.
  • 该模型展示了用于预测表面磁性排序的概括能力.

结论:

  • 开发的框架增强了UMLIP在磁性材料研究中的实用性.
  • 这种方法加速了磁性材料的计算发现.
  • 异常检测可以提高材料科学中的机器学习模型的准确性和稳定性.