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¹H NMR: Long-Range Coupling01:27

¹H NMR: Long-Range Coupling

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The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene...
1.7K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.1K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.1K
Neuronal Communication01:28

Neuronal Communication

777
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
777
Action Potential01:31

Action Potential

7.8K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they...
7.8K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

1.1K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
1.1K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

10.3K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
10.3K

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相关实验视频

Updated: Jun 4, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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图形神经网络如何推断原子间潜力:传递信息算法的作用.

Sungwoo Kang1

  • 1Computational Science Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.

The Journal of chemical physics
|December 23, 2024
PubMed
概括
此摘要是机器生成的。

图表神经网络原子间潜力 (GNN-IPs) 学习非局部静电相互作用,解释它们将其推断到新材料结构的能力. 这种能力对于在各种未经训练的领域中准确预测至关重要.

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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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相关实验视频

Last Updated: Jun 4, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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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
  • 机器学习 机器学习

背景情况:

  • 图表神经网络原子间潜力 (GNN-IPs) 对材料建模具有前景.
  • 在晶体数据上训练的通用GNN-IP通常能很好地推断到表面和无形结构.
  • 这种推断能力的理论基础仍然不清楚.

研究的目的:

  • 为GNN-IPs的推断能力提供理论解释.
  • 调查GNN-IP如何学习和预测未经训练的几何中的交互.
  • 识别影响普遍潜力的推断表现的因素.

主要方法:

  • 证明了GNN-IPs通过消息传递捕获非局部静电相互作用的能力.
  • 在玩具模型和用于静电力预测的DFT数据上测试了GNN-IP模型 (SevenNet,MACE).
  • 在通用GNN-IP中分析了库伦和动能术语的推断.
  • 研究了超参数对外推性能的影响.

主要成果:

  • GNN-IPs准确地预测未经训练的领域中的静电力,学习库伦相互作用的功能形式.
  • 能够学习非局部静电学,结合GNN嵌入,解释了外推.
  • 在未经训练的领域,SevenNet-0成功地推断出库伦相互作用,而不是动能力量.
  • 超参数显著影响抽象性能,并确定了特定的局限性.

结论:

  • GNN-IP 的外推能力源于学习非局部静电相互作用.
  • 嵌性质的GNN进一步有助于这种现象.
  • 了解这些机制是开发更强大的通用原子间潜力的关键.