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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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The de Broglie Wavelength02:32

The de Broglie Wavelength

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In the macroscopic world, objects that are large enough to be seen by the naked eye follow the rules of classical physics. A billiard ball moving on a table will behave like a particle; it will continue traveling in a straight line unless it collides with another ball, or it is acted on by some other force, such as friction. The ball has a well-defined position and velocity or well-defined momentum, p = mv, which is defined by mass m and velocity v at any given moment. This is the typical...
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Molecular and Ionic Solids02:54

Molecular and Ionic Solids

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Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
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Standing Waves in a Cavity01:28

Standing Waves in a Cavity

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A household microwave and lasers are examples of standing electromagnetic waves in a cavity. When two conducting metal plates are placed parallel at the nodal planes, it creates a cavity where standing waves are formed. The cavity between the two planes is analogous to a stretched string held at the points x = 0 and x = L. Here, the distance 'L' between the two planes must be an integer multiple of half of the wavelength. The wavelengths that satisfy this condition are given by:
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Graphing the Wave Function01:13

Graphing the Wave Function

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Consider the wave equation for a sinusoidal wave moving in the positive x-direction. The wave equation is a function of both position and time. From the wave equation, two different graphs can be plotted.
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Travelling Waves01:04

Travelling Waves

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A wave is a disturbance that propagates from its source, repeating itself periodically, and is typically associated with simple harmonic motion. Mechanical waves are governed by Newton's laws and require a medium to travel. A medium is a substance in which a mechanical wave propagates, and the medium produces an elastic restoring force when it is deformed.
Water waves, sound waves, and seismic waves are some examples of mechanical waves. For water waves, the wave propagation medium is...
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Updated: Jan 14, 2026

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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对于固体的可转移的神经波函数.

L Gerard1, M Scherbela1, H Sutterud2

  • 1Faculty of Mathematics, University of Vienna, Vienna, Austria.

Nature computational science
|October 22, 2025
PubMed
概括
此摘要是机器生成的。

深度学习通过在多个固态系统中优化单个神经网络来加速量子化学计算. 这种方法显著降低了模拟材料的计算成本.

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Characterization of Thermal Transport in One-dimensional Solid Materials
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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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相关实验视频

Last Updated: Jan 14, 2026

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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Characterization of Thermal Transport in One-dimensional Solid Materials
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科学领域:

  • 量子化学 是一个量子化学.
  • 计算材料科学科学 计算材料科学
  • 科学中的人工智能.

背景情况:

  • 基于深度学习的变量蒙特卡洛 (DL-VMC) 为许多电子的施罗丁格方程提供了高精度.
  • 由于优化神经网络重量的高计算成本,DL-VMC方法面临着挑战,因为每个新系统都需要优化神经网络重量.

研究的目的:

  • 将优化单个神经网络在多个系统中的方法扩展到固态材料.
  • 为了降低与模拟固体相关的计算成本,这些固体涉及不同的几何形状和条件.

主要方法:

  • 实现了一个单个神经网络,在各种几何形状,边界条件和超级细胞大小中进行优化,用于固态计算.
  • 将预训练的神经网络从较小的 (2x2x2) 转移到较大的 (3x3x3) LiH超级细胞.

主要成果:

  • 在不同的固态变异中优化单个替代品显著减少了所需的优化步骤的数量.
  • 使用转移网络模拟较大的3x3x3LiH超级电池,与以前的方法相比,需要减少50倍的优化步骤.

结论:

  • 在多个固态系统中优化单个神经网络是减少计算开销的可行策略.
  • 这种转移学习方法显示了高效的高通量材料模拟的前景.