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相关概念视频

Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

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Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
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Energetics of Solution Formation02:35

Energetics of Solution Formation

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The formation of a solution is an example of a spontaneous process, which is 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. Formation of the solution requires the solute–solute and solvent–solvent...
7.3K
Free Energy and Equilibrium00:55

Free Energy and Equilibrium

8.6K
The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔG is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
The reaction quotient, Q, is a convenient measure of the...
8.6K
Free Energy and Equilibrium02:56

Free Energy and Equilibrium

27.0K
The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔGrxn is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
Recall that Q is the numerical value of the mass action...
27.0K
Energy Transfer in Chemical Reactions01:16

Energy Transfer in Chemical Reactions

10.6K
Chemical reactions require sufficient energy to cause the matter to collide with enough precision and force that old chemical bonds can be broken and new ones formed. In general, kinetic energy is the form of energy powering any type of matter in motion. Imagine a person building a brick wall. The energy it takes to lift and place one brick on top of another is the kinetic energy—the energy matter possesses because of its motion. Once the wall is in place, it stores potential energy.
10.6K
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

1.6K
Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
1.6K

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相关实验视频

Updated: Jan 16, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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强化学习与形成能量反用于材料扩散模型.

Jiao Huang1, Qianli Xing2, Jinglong Ji1

  • 1Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China; College of Artificial Intelligence, Jilin University, Changchun, Jilin, 130012, China.

Neural networks : the official journal of the International Neural Network Society
|October 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的强化学习框架 (RLFEF),以增强稳定晶体材料发现的生成模型. 该方法提高了产生高质量,稳定的晶体结构的成功率.

关键词:
人工智能的人工智能是人工智能.预测晶体结构的预测扩散模型是一个扩散模型.图表神经网络的神经网络强化学习是一种强化学习.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能
  • 计算化学计算化学

背景情况:

  • 生成模型,特别是扩散模型,越来越多地用于高效的材料发现.
  • 将物理约束和对称性纳入扩散模型可以提高晶体生成质量.
  • 由于数据限制和复杂性,准确捕捉稳定的晶体结构仍然存在挑战.

研究的目的:

  • 提出一种新的微调框架,即强化学习微调与能量反 (RLFEF),以提高生成的晶体材料的稳定性.
  • 提高生成模型在产生稳定的晶体结构方面的成功率.

主要方法:

  • 制定了材料扩散过程作为马尔科夫决策过程,使用形成能量作为奖励.
  • 证明了在强化学习中优化预期回报和将政策梯度更新应用于扩散模型之间的等价性.
  • 证明微调模型尊重晶体材料固有的对称性.

主要成果:

  • 该RLFEF框架在多个任务中实现了最先进的性能.
  • 在属性优化,初始生成,晶体结构预测和一般材料生成方面取得了卓越的结果.
  • 在生成的晶体材料结构中展示了更好的稳定性和精度.

结论:

  • 该RLFEF框架为生成材料科学提供了重大进展.
  • 这种方法有效地解决了现有模型在产生稳定的晶体材料方面的局限性.
  • 该方法有望加速新型功能材料的发现.