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相关概念视频

Logarithmic Differentiation01:28

Logarithmic Differentiation

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When a car’s weight and driving forces act on a tire, they impose an external load on the rubber material. This load is resisted internally by forces distributed throughout the tire structure, which are defined as stress. The resulting deformation of the rubber due to this stress is quantified as strain. The relationship between stress and strain governs how the tire deforms under load and is central to understanding its mechanical response during operation.Rubber exhibits a nonlinear...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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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Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

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James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Implicit Differentiation01:25

Implicit Differentiation

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In classical mechanics, motion is often described through relationships between spatial coordinates and time. A car moving along a straight highway with constant acceleration serves as a simple case where velocity is an explicit function of time. This scenario results in a linear equation, enabling straightforward analysis using basic differentiation techniques.In contrast, a satellite in circular orbit follows a path defined by an implicit function. The position of the satellite is constrained...
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相关实验视频

Updated: Jan 13, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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平面波DFT的算法差异化:材料设计,错误控制和学习模型参数.

Niklas Frederik Schmitz1,2, Bruno Ploumhans1,2, Michael F Herbst1,2

  • 1Mathematics for Materials Modelling (MatMat), Institute of Mathematics & Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

npj computational materials
|January 9, 2026
PubMed
概括
此摘要是机器生成的。

我们介绍了一个新的框架,它结合了算法分化 (AD) 和密度函数扰动理论 (DFPT),用于材料建模中的准确计算. 这种方法自动化了衍生计算,使逆向设计和参数学习等高级应用程序成为可能.

关键词:
化学 化学 化学材料科学是一种材料科学.数学和计算的数学和计算.物理 物理学 物理

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科学领域:

  • 计算材料科学科学 计算材料科学
  • 量子化学 是一个量子化学.
  • 固态物理 固态物理

背景情况:

  • 密度功能理论 (DFT) 是材料建模的基石.
  • 计算DFT输出对输入参数的衍生值至关重要,但往往很复杂.
  • 现有的方法需要手动导出梯度表达式,限制了适用性.

研究的目的:

  • 开发一个统一的框架,用于DFT中自动化衍生品计算.
  • 结合算法差异化 (AD) 和密度函数扰动理论 (DFPT) 的优势.
  • 为了能够准确计算任何DFT输出对任何输入参数的导数.

主要方法:

  • 在DFPT框架内实施前式AD方法.
  • 将AD-DFPT方法集成到密度功能工具包 (DFTK) 中.
  • 通过各种应用程序进行验证,包括反向设计和不确定性传播.

主要成果:

  • 该AD-DFPT框架准确地计算导数,而不需要手动梯度导数.
  • 在各种材料建模任务中证明了广泛的适用性.
  • 成功应用于半导体带间隙的反向设计和学习交换-相关函数参数.

结论:

  • 通过自动化衍生计算,AD-DFPT框架显著推进了第一原则材料建模.
  • 通过渐变驱动的工作流程开辟新的研究途径.
  • 促进复杂的任务,如参数优化和不确定性量化在材料科学.