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相关概念视频

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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通过基于扩散的深度生成模型对2D/3D随机材料的微结构重建.

Xianrui Lyu1, Xiaodan Ren2

  • 1College of Civil Engineering, Tongji University, Shanghai, 200092, People's Republic of China.

Scientific reports
|February 29, 2024
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概括
此摘要是机器生成的。

本研究介绍了无声扩散概率模型 (DDPM) 以准确地重建材料微观结构. 这些先进的生成模型能够精确控制随机性,并创建梯度和具有特定属性的3D材料,如透性.

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科学领域:

  • 材料科学与工程 材料科学与工程
  • 计算材料科学科学 计算材料科学
  • 材料设计中的人工智能

背景情况:

  • 建立过程结构属性 (PSP) 关系对于材料设计至关重要.
  • 像VAE和GAN这样的现有生成模型在学习复杂数据分布方面存在局限性.
  • 精确的微观结构重建是材料信息学的一个关键挑战.

研究的目的:

  • 开发和应用先进的生成模型,用于高保真材料微观结构的重建.
  • 探索除了传统方法之外,消除扩散概率模型 (DDPM) 的能力.
  • 允许有条件生成具有可控属性的微结构,包括3D结构和透性.

主要方法:

  • 利用无声扩散概率模型 (DDPM) 来学习高维微观结构数据的概率分布.
  • 用于微结构随机性调节和通过潜空间插曲生成梯度材料的消极扩散隐性模型 (DDIM).
  • 将2D重建扩展到3D,通过特征编码集成透性,并通过格子博尔兹曼方法验证.

主要成果:

  • 成功地重建了各种微观结构 (包括,旋,棋盘,碎形噪声) 以高保真度.
  • 实现了对微观结构随机性的定量控制,并生成了梯度材料.
  • 启用了有针对性透性的3D多孔微结构的条件生成,通过格子博尔兹曼模拟验证.

结论:

  • DDPM提供了一个强大的框架来学习复杂的微结构分布,并实现准确的重建.
  • 开发的方法为材料反向设计和属性导向微结构生成提供了新的方法.
  • 这项工作为先进的计算材料设计铺平了道路,通过弥合生成建模和物理属性预测.