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相关概念视频

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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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).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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First Law: Particles in Two-dimensional Equilibrium01:18

First Law: Particles in Two-dimensional Equilibrium

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Recall that a particle in equilibrium is one for which the external forces are balanced. Static equilibrium involves objects at rest, and dynamic equilibrium involves objects in motion without acceleration; but it is important to remember that these conditions are relative. For instance, an object may be at rest when viewed from one frame of reference, but that same object would appear to be in motion when viewed by someone moving at a constant velocity.
Newton's first law tells us about...
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First Law: Particles in One-dimensional Equilibrium01:10

First Law: Particles in One-dimensional Equilibrium

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Newton's first law of motion states that a body at rest remains at rest, or if in motion, remains in motion at constant velocity, unless acted on by a net external force. It also states that there must be a cause for any change in velocity (a change in either magnitude or direction) to occur. This cause is a net external force. For example, consider what happens to an object sliding along a rough horizontal surface. The object quickly grinds to a halt, due to the net force of friction. If...
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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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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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相关实验视频

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Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles
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Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

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机器学习的密度功能对无异型斑块粒子的异型斑块粒子.

Alessandro Simon1,2, Jens Weimar1, Georg Martius2

  • 1Institute for Applied Physics, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany.

Journal of chemical theory and computation
|January 17, 2024
PubMed
概括

这项研究模型使用异构的斑点粒子和经典密度函数理论来关联流体. 机器学习构建了一个定向内核,改进了墙壁附近密度分布的近似值.

科学领域:

  • 统计力学就是统计力学.
  • 软物质物理学 软物质物理学
  • 计算化学是一种计算化学.

背景情况:

  • 不同类型的斑块粒子是结合流体的关键模型.
  • 经典密度函数理论 (DFT) 用于研究流体行为.
  • 了解墙壁附近的密度分布对于材料性能至关重要.

研究的目的:

  • 为Kern-Frenkel模型开发基于机器学习 (ML) 的方法.
  • 描述平面墙壁附近的异型粒子的平衡密度分布.
  • 改进现有的方位相关性近似方法.

主要方法:

  • 针对异性异性斑点颗粒的DFT方法的制定.
  • 将密度函数分为参考和定向部分.
  • 使用ML和模拟数据开发用于定向部分的平均场内核.

主要成果:

  • 基于ML的内核准确地描述了定位和定向分辨密度.
  • 这种方法比随机相近似方法更好地捕捉墙壁附近的方向相关性.
  • 确定了平均场处理的成功和局限性.

结论:

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  • 基于ML的DFT方法为研究结合流体提供了一个强大的工具.
  • 与传统的近似方法相比,这种方法为方向相关性提供了更高的准确性.
  • 未来的工作旨在开发一个完全基于ML的密度函数.