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相关概念视频

Energy Bands in Solids01:01

Energy Bands in Solids

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Isolated atoms have discrete energy levels that are well described by the Bohr model. And, it quantifies the energy of an electron in a hydrogen atom as En. Higher quantum numbers 'n' yield less negative, closer electron energy levels.
 Band Formation:
When atoms are brought close together, as in a solid, these discrete energy levels begin to split due to the overlap of electron orbitals from adjacent atoms. This split occurs because of the Pauli exclusion principle, which states...
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Kinematic Equations - II01:17

Kinematic Equations - II

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The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
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Kinematic Equations - III01:18

Kinematic Equations - III

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The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
Using the kinematic equations,...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Fermi Level Dynamics01:12

Fermi Level Dynamics

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The vacuum level denotes the energy threshold required for an electron to escape from a material surface. It is usually positioned above the conduction band of a semiconductor and acts as a benchmark for comparing electron energies within various materials.
Electron affinity in semiconductors refers to the energy gap between the minimum of its conduction band and the vacuum level and it is a critical parameter in determining how easily a semiconductor can accept additional electrons.
The work...
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Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

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It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
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谱图:将慢动力学嵌入到集体变量中

Jakub Rydzewski1

  • 1Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Toruń, Poland.

The journal of physical chemistry letters
|June 1, 2023
PubMed
概括

我们开发了一种深度学习方法,即光谱图,用于识别关键集体变量 (CV),以了解复杂的物理系统动态. 这种方法有效地捕捉了高维数据中的缓慢转换.

科学领域:

  • 物理化学 物理化学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 复杂的物理系统通常需要高维数据表示.
  • 识别有意义的集体变量 (CV) 是至关重要的,但具有挑战性.
  • 描述缓慢的动力学和金属稳定状态之间的罕见过渡是特别困难的.

研究的目的:

  • 提出一种无监督的深度学习方法来构建缓慢的集体变量 (CV).
  • 为了应对识别CV的挑战,这些CV捕捉了物理系统中的缓慢动态.
  • 提高对高维系统中罕见过渡的理解.

主要方法:

  • 开发了一种名为光谱图的无监督深度学习方法.
  • 通过最大化过渡矩阵的光谱差距来构建缓慢的CV.
  • 估计过渡矩阵使用一种异型扩散核.

主要成果:

  • 在高维系统中成功识别了慢集体变量 (CV).
  • 证明了该方法在捕捉慢动力学方面的有效性.
  • 将光谱图方法应用于可逆折叠过程.

结论:

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  • 谱图方法提供了一种有效的方法来识别缓慢的CV.
  • 这种深度学习技术增强了对复杂物理系统动态的分析.
  • 该方法对研究化学和物理中的罕见事件和过渡具有前景.