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相关概念视频

Van der Waals Interactions01:24

Van der Waals Interactions

64.0K
Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
64.0K
Intermolecular Forces03:13

Intermolecular Forces

58.4K
Atoms and molecules interact through bonds (or forces): intramolecular and intermolecular. The forces are electrostatic as they arise from interactions (attractive or repulsive) between charged species (permanent, partial, or temporary charges) and exist with varying strengths between ions, polar, nonpolar, and neutral molecules. The different types of intermolecular forces are ion–dipole, dipole–dipole, hydrogen bonds, and dispersion; among these, dipole–dipole, hydrogen...
58.4K
Intermolecular Forces and Physical Properties02:56

Intermolecular Forces and Physical Properties

20.8K
20.8K
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

51.2K
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,...
51.2K
Intermolecular vs Intramolecular Forces03:00

Intermolecular vs Intramolecular Forces

87.4K
Intermolecular forces (IMF) are electrostatic attractions arising from charge-charge interactions between molecules. The strength of the intermolecular force is influenced by the distance of separation between molecules. The forces significantly affect the interactions in solids and liquids, where the molecules are close together. In gases, IMFs become important only under high-pressure conditions (due to the proximity of gas molecules). Intermolecular forces dictate the physical properties of...
87.4K
Intermolecular Forces in Solutions02:28

Intermolecular Forces in Solutions

33.8K
The formation of a solution is an example of a spontaneous process, a process that occurs under specified conditions without energy from some external source.
When the strengths of the intermolecular forces of attraction between solute and solvent species in a solution are no different than those present in the separated components, the solution is formed with no accompanying energy change. Such a solution is called an ideal solution. A mixture of ideal gases (or gases such as helium and argon,...
33.8K

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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来自机器学习数据集的分子间非结合相互作用.

Jia-An Chen1, Sheng D Chao1,2

  • 1Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.

Molecules (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

机器学习准确地模拟了用于分子动力学模拟的聚合物相互作用. 这种方法平衡了准确性和计算成本,为更好的模拟铺平了道路.

关键词:
人工智能的人工智能是人工智能.机器学习潜力 机器学习潜力不结合的相互作用.量子化学数据集中的量子化学数据集.对称性适应扰动理论对称性适应扰动理论

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科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 聚合物科学 聚合物科学

背景情况:

  • 精确的聚合物系统分子动力学 (MD) 模拟需要精确确定分子间非结合相互作用.
  • 在部队现场开发中平衡精度和计算成本仍然是一个重大挑战.
  • 将计算的能量数据表示为连续力函数是力场建模中的一个关键困难.

研究的目的:

  • 使用机器学习开发一种用于有机聚合物的通用分子间非结合相互作用力场.
  • 解决 polimer 系统中准确有效地建模非共价相互作用的挑战.

主要方法:

  • 作为培训套件,使用了经过充分记录的初始数据集 (SOFG-31).
  • 采用了CLIFF内核类型机器学习方案来预测相互作用能量.
  • 专注于从SOFG-31数据集中选择的异构体,用于培训和测试.

主要成果:

  • 总体错误远低于大约1kcal/mol的化学准确度值.
  • 证明了机器学习在预测分子间相互作用能量的有效性.
  • 验证了用于力场建模的机器学习方案的可行性.

结论:

  • 机器学习技术对于开发精确且计算效率高的力场来进行聚合物模拟具有显著的前景.
  • 开发的力场有效地模拟了有机聚合物中的分子间非结合相互作用.
  • 这种方法为分子动力学力场开发中的准确性-成本权衡提供了可行的解决方案.