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相关概念视频

Inductive Effects on Chemical Shift: Overview01:27

Inductive Effects on Chemical Shift: Overview

1.1K
The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.8K
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

585
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
585
¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

811
At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
811
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

49.4K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
49.4K

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对于数据驱动化学的预测刚性.

Sanggyu Chong1, Filippo Bigi1, Federico Grasselli1

  • 1Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. michele.ceriotti@epfl.ch.

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概括
此摘要是机器生成的。

预测刚性增强了对化学中的机器学习 (ML) 模型的理解. 这些指标评估了全球和本地ML模型的稳定性,提高了化学结构-属性相关性培训效率和可解释性.

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科学领域:

  • * 计算化学 计算机化学
  • * 化学科学中的机器学习

背景情况:

  • * 机器学习 (ML) 在化学科学中越来越重要,用于将结构与属性相关联.
  • *了解ML模型的学习,可解释性和可转移性对于高效的应用至关重要.
  • * 目前用于评估ML模型行为的方法缺乏详细的本地洞察力.

研究的目的:

  • * 介绍和证明预测刚性的实用性,用于分析化学中的ML模型.
  • * 评估ML模型在全球和本地预测层面的稳定性.
  • *以指导数据集的构建和提高ML模型培训效率和可解释性.

主要方法:

  • *从ML模型的损失函数中导出预测刚度作为一组指标.
  • * 应用预测刚性来评估ML模型性能和学习行为.
  • * 在原子模拟中对粗粒度ML模型实施预测刚度.

主要成果:

  • * 预测刚性有效地评估ML模型在全球和组件智能预测水平的稳定性.
  • * 这些指标提供了对各种ML模型的学习动态的见解.
  • * 该研究表明,通过预测刚性分析指导改进了数据集构建策略.
  • * 预测刚度的适用性显示为粗粒度的ML模型.

结论:

  • * 预测刚性为理解和改进化学应用中的ML模型提供了强大的工具.
  • *这些指标通过揭示本地预测行为来提高ML模型的可解释性和可转移性.
  • *这种方法有助于更高效的数据集策划和化学结构属性预测的模型开发.