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相关概念视频

Carrier Transport01:21

Carrier Transport

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The generation of electrical current in semiconductors is fundamentally driven by two mechanisms: drift and diffusion. These processes are essential for the functionality and performance of semiconductor-based devices.
Drift Current:
The drift of charge carriers is started by an external electric field (E). Charged particles, such as electrons and holes, experience an acceleration between collisions with lattice atoms. For electrons, this results in a drift velocity (vd) given by:
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Theory of Metallic Conduction01:17

Theory of Metallic Conduction

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The conduction of free electrons inside a conductor is best described by quantum mechanics. However, a classical model makes predictions close to the results of quantum mechanics. It is called the theory of metallic conduction.
In this theory, Newton's second law of motion is used to determine the acceleration of an electron in the presence of an applied electric field. Then, its velocity is expressed via this acceleration.
An electron moves through the crystal, containing positive ions,...
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Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

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Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
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Drift Velocity01:19

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The high speed of electrical signals results from the fact that the force between charges acts rapidly at a distance. Thus, when a free charge is forced into a wire, the incoming charge pushes other charges ahead due to the repulsive force between like charges. These moving charges move the charges farther down the line. The density of charge in a system cannot easily be increased, so the signal is passed on rapidly. The resulting electrical shock wave moves through the system at nearly the...
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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 Bohr Model02:18

The Bohr Model

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Following the work of Ernest Rutherford and his colleagues in the early twentieth century, the picture of atoms consisting of tiny dense nuclei surrounded by lighter and even tinier electrons continually moving about the nucleus was well established. This picture was called the planetary model since it pictured the atom as a miniature “solar system” with the electrons orbiting the nucleus like planets orbiting the sun. The simplest atom is hydrogen, consisting of a single proton as...
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相关实验视频

Updated: Jun 15, 2025

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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研究扩散模型的行为,以加速电子结构计算.

Daniel Rothchild1, Andrew S Rosen2,3, Eric Taw4,3

  • 1Department of Electrical Engineering and Computer Science, University of California Berkeley USA drothchild@berkeley.edu.

Chemical science
|August 26, 2024
PubMed
概括

扩散模型通过学习潜在能量表面来加速分子生成. 它们的放松阶段有效地发现低能分子几何形状,加速电子结构计算.

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Last Updated: Jun 15, 2025

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科学领域:

  • 计算化学计算化学
  • 机器学习在化学中的应用
  • 分子建模分子建模

背景情况:

  • 扩散模型为加速分子生成和电子结构计算提供了一个有希望的途径.
  • 目前的方法通常需要昂贵的第一原则数据集来训练原子间潜力.
  • 了解扩散模型预测的基于物理的基础对于它们的应用至关重要.

研究的目的:

  • 为了研究新分子生成的扩散模型.
  • 将扩散模型预测与基于物理的计算进行比较.
  • 探索扩散模型在加速电子结构计算方面的潜力.

主要方法:

  • 对分子生成的流行的扩散模型的分析.
  • 检查模型的推断过程,包括探索和放松阶段.
  • 评估模型学习潜在能量表面结构的能力.
  • 重新利用模型的放松阶段进行形状采样和结构放松.

主要成果:

  • 扩散模型的推断涉及不同的探索 (原子物种选择) 和放松 (几何优化) 阶段.
  • 在训练过程中,模型逐渐学习了潜在能量表面的第一阶段结构,然后是更高阶段结构.
  • 放松阶段有效地采样了博尔兹曼分布,并执行结构放松,产生比经典力场能量明显低的几何形状.
  • 使用扩散产生的结构初始化密度函数理论 (DFT) 放松,与使用经典力场放松结构相比,其速度提高了2倍以上.

结论:

  • 扩散模型可以高效地产生低能分子几何形状,与经典力场相比或更好.
  • 扩散模型的放松阶段是分子结构优化和 conformational 采样的一种多功能工具.
  • 扩散模型显示了加速昂贵的电子结构计算的巨大潜力,如DFT.