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相关概念视频

Energy Bands in Solids01:01

Energy Bands in Solids

786
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...
786
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.4K
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
1.4K
Bandpass Sampling01:17

Bandpass Sampling

166
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
166
Band Theory02:35

Band Theory

15.0K
When two or more atoms come together to form a molecule, their atomic orbitals combine and molecular orbitals of distinct energies result. In a solid, there are a large number of atoms, and therefore a large number of atomic orbitals that may be combined into molecular orbitals. These groups of molecular orbitals are so closely placed together to form continuous regions of energies, known as the bands.
The energy difference between these bands is known as the band gap.
Conductor, Semiconductor,...
15.0K
Fermi Level Dynamics01:12

Fermi Level Dynamics

228
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...
228
UV–Vis Spectroscopy: Molecular Electronic Transitions01:16

UV–Vis Spectroscopy: Molecular Electronic Transitions

1.4K
In Ultraviolet–Visible (UV–Vis) spectroscopy, the absorption of electromagnetic radiation is used to probe the electronic structure of molecules. This technique provides insights into molecular electronic transitions, particularly the movement of electrons between different molecular orbitals. Radiation is absorbed if the energy of the electromagnetic radiation passing through the molecule is precisely equal to the energy difference between the excited and ground states. During this...
1.4K

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相关实验视频

Updated: Jun 15, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

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在带间隙上训练机器学习的密度函数.

Kyle Bystrom1, Stefano Falletta1, Boris Kozinsky1,2

  • 1Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts 02138, United States.

Journal of chemical theory and computation
|August 23, 2024
PubMed
概括
此摘要是机器生成的。

机器学习密度函数解决了密度函数理论 (DFT) 中的带隙问题. 这种新方法准确地预测了分子和材料的电子性质.

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
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Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials

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相关实验视频

Last Updated: Jun 15, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

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ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

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Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
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科学领域:

  • 计算化学计算化学
  • 材料科学 材料科学 材料科学
  • 量子力学就是量子力学.

背景情况:

  • 半语言密度函数理论 (DFT) 系统地低估了带间隙,这是一个根本性的挑战.
  • 这种带隙问题阻碍了对电子属性的准确预测和研究电荷传输机制,原因是自我相互作用和移位错误.

研究的目的:

  • 开发一种机器学习方法来设计密度函数,准确预测单粒子能量水平.
  • 在电子财产预测中克服传统DFT的局限性.

主要方法:

  • 在机器学习密度函数中采用高斯过程,明确适应单粒子能量水平.
  • 引入了密度矩阵的非局部特征,以捕获必要的电子信息.
  • 训练了一台机器学习的功能来准确交换能量.

主要成果:

  • 训练有素的功能精确地预测了分子能量差距和反应能量,与混合DFT计算有很好的一致性.
  • 该模型通过预测固体中的极子形成能量来证明可转移性和稳定性,尽管它仅在分子数据上进行训练.

结论:

  • 这种机器学习方法为开发用于准确预测分子和材料电子性质的先进功能提供了有希望的途径.
  • 该方法可以扩展到完整的交换相关函数,推进DFT功能.