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相关概念视频

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

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Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
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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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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Molecular Geometry and Dipole Moments02:36

Molecular Geometry and Dipole Moments

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The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
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Diffusion01:12

Diffusion

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Diffusion01:21

Diffusion

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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相关实验视频

Updated: Mar 18, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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图表基于潜在扩散的分子表示学习,用于在分子性质预测中进行增强的概括.

Daiki Koge1, Naoaki Ono2, Takashi Abe3

  • 1Department of Electrical and Information Engineering, Graduate School of Science and Technology, Niigata University, Ikarashi, Niigata, 950-2181, Japan. daiki-ko@ie.niigata-u.ac.jp.

Journal of cheminformatics
|March 17, 2026
PubMed
概括

隐性扩散模型增强分子表示学习,以更好地预测属性. 一个新型模型的图形LDA显示了由于光滑和多式联络潜伏表示而改善的概括性.

关键词:
否认扩散的概率模型.概括表现的表现一般化潜在扩散模型的潜伏扩散模型.分子表示的分子表示.变压器变压器变压器

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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

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相关实验视频

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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
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科学领域:

  • 计算化学的计算化学
  • 机器学习 机器学习
  • 药物发现 药物发现 药物发现

背景情况:

  • 分子表示学习对于预测化学性质至关重要.
  • 传统的方法经常与一般化作斗争.
  • 深度生成模型为改进的表示提供了潜力.

研究的目的:

  • 评估隐性扩散模型对分子表示学习的影响.
  • 在分子性质预测中评估概括性能.
  • 分析有助于改进概括的因素.

主要方法:

  • 制定了一个深度生成模型,使用一个潜在的扩散前.
  • 引入了评估指标:广泛适用的信息标准 (WAIC) 和广泛适用的贝叶斯信息标准 (WBIC).
  • 开发了一个基于流性和多模式的分析框架.
  • 构建了图形潜伏扩散自编码器 (图形LDA).

主要成果:

  • 与其他模型相比,图 LDA 显示出优越的泛化性能.
  • 基于隐性扩散的先验始终改善了概括.
  • 分析证实,潜伏表示的流性和多模式性推动了卓越的性能.
  • 在不同模型中观察到不同的概括行为.

结论:

  • 隐性扩散模型显著增强了用于属性预测的分子表示学习.
  • 图形LDA的架构,利用隐性扩散先验,产生强大的和可概括的分子表示.
  • 这些发现为开发高泛化分子表示学习模型提供了原则性的理解和指导方针.