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相关概念视频

Colors and Magnetism03:02

Colors and Magnetism

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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...
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Valence Bond Theory02:42

Valence Bond Theory

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Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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相关实验视频

Updated: Jun 5, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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对于Co3O4来说,一个高维的神经网络的潜力.

Amir Omranpour1, Jörg Behler2

  • 1Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany.

Journal of physics. Condensed matter : an Institute of Physics journal
|December 13, 2024
PubMed
概括
此摘要是机器生成的。

机器学习潜力 (MLP) 现在使氧化物 (Co3O4) 旋转催化剂的精确分子动力学模拟成为可能. 这项研究开发了Co3O4的MLP,揭示了其取决于温度的特性,以改善催化研究.

关键词:
氧化物 氧化物在HDNNP中使用HDNNP.模拟MD的模拟方法在MLP中,MLP是MLP.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 催化剂是一种催化剂.

背景情况:

  • 氧化物 (Co3O4) 旋转是氧化催化过程中至关重要的.
  • 准确模拟Co3O4的特性需要精确的原子相互作用描述.
  • 现有的方法难以模拟Co3O4的复杂性,限制了模拟尺度.

研究的目的:

  • 为了开发一个机器学习潜力 (MLP) Co3O4 spinel.
  • 为了使Co3O4.4的大规模分子动力学模拟成为可能.
  • 为了研究Co3O4.4的取决于温度的特性.

主要方法:

  • 为Co3O4.4开发了一个高维神经网络潜力 (HDNNP).
  • 使用密度函数理论 (DFT) 计算训练了MLP.
  • 通过计算结构性,振动性和动态性质来验证MLP.

主要成果:

  • 成功构建并验证了一种用于散装Co3O4螺旋体的MLP.
  • 模拟显示了关键的温度依赖性质,包括热膨胀.
  • 该MLP准确地捕获了Co3O4.4中的复杂相互作用.

结论:

  • MLP提供了一种强大的方法来模拟复杂的材料,如Co3O4.
  • 这项工作为在现实条件下研究CO3O4催化剂提供了经过验证的工具.
  • 这些发现为催化应用推进了计算材料科学.