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相关概念视频

Atomic Radii and Effective Nuclear Charge03:08

Atomic Radii and Effective Nuclear Charge

51.8K
The elements in groups of the periodic table exhibit similar chemical behavior. This similarity occurs because the members of a group have the same number and distribution of electrons in their valence shells.
51.8K
Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

1.0K
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.
1.0K
Density00:56

Density

14.8K
Density is an important characteristic of substances, crucial in determining whether an object sinks or floats in a fluid. Its SI unit is kg/m3, and its cgs unit is g/cm3. The density of an object helps in identifying its composition, and also reveals information about the phase of the matter and its substructure. The densities of liquids and solids are roughly comparable, consistent with the fact that their atoms are in close contact. However, gases have much lower densities than liquids and...
14.8K
Atomic Nuclei: Types of Nuclear Relaxation01:28

Atomic Nuclei: Types of Nuclear Relaxation

324
Nuclear relaxation restores the equilibrium population imbalance and can occur via spin–lattice or spin–spin mechanisms, which are first-order exponential decay processes.
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers...
324
Atomic Weight01:25

Atomic Weight

9.0K
Protons and neutrons have approximately the same mass, about 1.67 × 10-24 grams. Scientists arbitrarily define this amount of mass as one atomic mass unit (amu) or one Dalton. Electrons are much smaller in mass than protons, weighing only 9.11 × 10-28 grams, or about 1/1800 of an atomic mass unit. As a result, they do not contribute much to an element's overall atomic mass. This means that, when considering atomic mass, it is customary to ignore the mass of any electrons and...
9.0K
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.1K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.1K

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Updated: Jul 21, 2025

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

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张量减小的原子密度表示

James P Darby1,2, Dávid P Kovács2, Ilyes Batatia2,3

  • 1Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom.

Physical review letters
|July 28, 2023
PubMed
概括
此摘要是机器生成的。

新的张量缩小表示提供了紧的原子环境描述器. 这种方法避免了化学元素的扩展问题,使机器学习中的更广泛的数据分析和回归任务成为可能.

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Author Spotlight: Optimizing Cryo-EM Analysis with CryoSieve for Enhanced Particle Selection Efficiency
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Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
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科学领域:

  • 计算材料科学科学 计算材料科学
  • 机器学习在化学和物理领域
  • 数据分析和数据表示.

背景情况:

  • 基于密度的原子环境表示对于原子模型中的机器学习至关重要.
  • 目前使用元素特异密度的张量积的方法,随着元素数量的增加,其可扩展性较差.
  • 图形神经网络通过学习嵌入提供了一个替代方案,但缺乏与基于密度的描述符的明确连接.

研究的目的:

  • 开发一个紧的,元素数量独立的局部原子环境的表示.
  • 通过张量分解将图形神经网络方法与传统的基于密度的描述符连接起来.
  • 为各种数据驱动的原子化任务创建一个系统地可改进的表示.

主要方法:

  • 重构图形神经网络原理作为邻近密度的基于描述符的张量分解.
  • 开发一种新的符号,以识别与现有数据压缩算法的连接.
  • 对原子环境进行张量减小表示的制定.

主要成果:

  • 成功地获得了局部原子环境的紧张量缩小表示.
  • 新表示的尺寸独立于所涉及的化学元素的数量.
  • 该方法证明了系统的收性和适用于各种数据分析和回归任务的适用性.

结论:

  • 拟议的缩小张量表示法克服了传统方法的可扩展性限制.
  • 这种方法弥合了基于密度的描述符和图形神经网络之间的差距.
  • 新的表示是用于材料科学和相关领域的机器学习的多功能工具.