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相关概念视频

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...
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NMR Spectroscopy: Spin–Spin Coupling01:08

NMR Spectroscopy: Spin–Spin Coupling

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The spin state of an NMR-active nucleus can have a slight effect on its immediate electronic environment. This effect propagates through the intervening bonds and affects the electronic environments of NMR-active nuclei up to three bonds away; occasionally, even farther. This phenomenon is called spin–spin coupling or J-coupling. Coupling interactions are mutual and result in small changes in the absorption frequencies of both nuclei involved. While nuclei of the same element are involved...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Neuroplasticity01:01

Neuroplasticity

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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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学习配对交互用于抽象和可解释的机器学习 原子间潜力与物理信息的神经网络.

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  • 1Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul 03722, Republic of Korea.

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概括

本研究介绍了P2Net,这是一个基于物理学的神经网络,用于机器学习原子间潜力. P2Net 增强了外推和可解释性,使复杂化学系统的准确模拟成为可能.

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科学领域:

  • 计算化学计算化学
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 机器学习原子间潜力 (ML-IPs) 难以推断和解释,特别是在数据稀缺的反应系统中.
  • 准确的原子模拟需要超越训练数据并提供物理洞察力的概括模型.

研究的目的:

  • 开发一种新的机器学习原子间潜力 (ML-IP),具有改进的外推能力和物理解释性.
  • 为了能够在极端条件下准确模拟复杂的材料和化学反应.

主要方法:

  • 介绍了一种对分解的物理信息神经网络 (P2Net).
  • 集成了一个分析的债券订单潜力 (BOP) 层,以解原子对的能量贡献.
  • 利用基本的物理原理来告知神经网络架构.

主要成果:

  • P2Net证明了超越培训数据的强有力的推断.
  • 实现了远离平衡的分子几何学的准确预测.
  • 双向能量分解促进了化学反应的详细分析,包括去质子和SN2反应.

结论:

  • 在ML-IP开发中,P2Net提高了数据效率.
  • 该模型为反应期间的原子间相互作用提供了更深入的见解.
  • 这种方法扩大了ML-IPs对复杂和反应性系统的适用性.