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相关概念视频

Electrical Conductivity01:13

Electrical Conductivity

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In perfect conductors, the electric field inside is always zero due to the abundance of free electrons, which nullify any field by flowing. As a result, any residual charge resides on the surface.
In a practical conductor, an applied electric field may be sustained, causing a flow of electrons, which produce a current. The differential form of the current, the current density, is related to the electric field.
More generally, it is related to the force per unit charge, which involves the...
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Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

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An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
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相关实验视频

Updated: Jul 18, 2025

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy
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用于超离子导体选的对比度量学学习.

Boyu Zhang1,2, Shuo Wang3, Fuchang Gao4

  • 1Institute for Modeling Collaboration and Innovation, University of Idaho, 875 Perimeter Dr MS 1122, Moscow, ID 83844-1122, USA.

SN computer science
|August 23, 2023
PubMed
概括

这项研究引入了一种新的机器学习框架,用于识别离子电池的高导电性材料. 该方法有效地选材料,即使数据有限,改善电池开发.

关键词:
导体的选 导体的选相反的学习学习.图表神经网络的神经网络材料科学 是一种材料科学.计量学学习的学习方法

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Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature
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科学领域:

  • 材料科学 材料科学 材料科学
  • 电化学 电化学 电化学
  • 计算机科学 计算机科学

背景情况:

  • 高性能离子电池对于现代技术至关重要.
  • 具有高导电性的材料对于先进的电池开发至关重要.
  • 由于缺乏验证的样本,预测高导体具有挑战性.

研究的目的:

  • 开发一个有效的度量学习框架,用于选高导电性材料.
  • 为了应对机器学习模型中有限的验证导体样本的挑战.
  • 为了提高电池应用的导电材料的预测准确度.

主要方法:

  • 利用一个语网络将材料结构映射到一个优化的特征空间.
  • 使用基于实例的方法对输入材料样本进行分类.
  • 开发了用于直接高导体选的度量学习框架.

主要成果:

  • 提出的方法有效地从不平衡的数据集中提取知识.
  • 在导体选中表现出良好的性能和概括能力.
  • 成功地将材料结构映射到一个有区别的特征空间.

结论:

  • 计量学习框架为高导体选提供了一个可行的解决方案.
  • 这种方法有望加速发现先进的电池材料.
  • 对不平衡数据的有效处理是拟议方法的一个关键优势.