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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

37
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
982
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

991
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
991

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使用与物理学相关的 priors 的扩散模型进行分子剥离.

Ishan Nadkarni1, J P Martínez Cordeiro1, Narayana R Aluru1

  • 1Walker Department of Mechanical Engineering, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin 78712, Texas, United States.

The journal of physical chemistry letters
|March 18, 2025
PubMed
概括

基于物理学的先验加速了对原子系统的否定扩散概率模型 (DDPMs). 这种方法利用统计力学来提高培训效率,加快生成高质量的分子样本.

科学领域:

  • 计算材料科学 计算材料科学
  • 机器学习用于物理.
  • 统计力学就是统计力学.

背景情况:

  • 否认扩散概率模型 (DDPMs) 在材料科学和分子建模的生成任务中表现出色.
  • 对于高质量的样本生成,DDPM需要进行许多代,从而导致采样速度缓慢.

研究的目的:

  • 通过结合物理知识的先验来增强原子系统的DDPM.
  • 加快采样过程,提高DDPM的培训效率.

主要方法:

  • 利用热力学和统计力学来推导先前分布的物理知情参数.
  • 使用这些衍生参数初始化马尔科夫链,使其更接近真实数据分布.
  • 应用该方法来从伦纳德-斯和多原子液体的原子配置中消除辐射分布函数.

主要成果:

  • 拟议的战略显著缩短了马尔科夫链的长度.
  • 提高了培训效率和加速了DDPM的抽样过程.
  • 证明了各种液体系统的辐射分布函数的有效消极化.

结论:

  • 从统计力学获得的基于物理学的先验提供了一个强大的策略,以在原子模拟中加速DDPMs.

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  • 这种方法通过解决采样速度限制,提高了DDPM在材料科学和分子建模中的实际适用性.