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相关概念视频

Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

1.1K
An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a low-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.
To...
1.1K
Emission Spectra02:39

Emission Spectra

50.7K
When solids, liquids, or condensed gases are heated sufficiently, they radiate some of the excess energy as light. Photons produced in this manner have a range of energies, and thereby produce a continuous spectrum in which an unbroken series of wavelengths is present.
50.7K
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
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

913
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
913
Parseval's Theorem for Fourier transform01:15

Parseval's Theorem for Fourier transform

868
Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
868
UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

6.9K
Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent...
6.9K

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相关实验视频

Updated: Jun 5, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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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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频谱运营商表示的表现.

Austin Zadoks1, Antimo Marrazzo2,3, Nicola Marzari1,4,5

  • 1Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.

npj computational materials
|December 5, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的机器学习框架,使用材料科学的电子结构描述器. 它能够准确地预测材料特性,并加速发现新的透明导电材料.

关键词:
电子属性和材料的电子属性和材料.电子结构 电子结构

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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 机器学习 机器学习

背景情况:

  • 材料科学中的机器学习通常集中在原子几何上,这对于学习光谱性质是不够的.
  • 学习像带隙这样的内在材料特性需要超越简单的原子环境的方法.

研究的目的:

  • 开发基于电子结构描述器的一般机器学习框架.
  • 将本框架用于材料相似性评估和加速选.

主要方法:

  • 开发了一个使用电子结构描述器的新框架.
  • 利用自然对称性和物理模型的可解释性.
  • 将框架应用于物质相似性和选任务.

主要成果:

  • 一个在217种材料上训练的模型在标记有希望的透明导电材料方面取得了75%的准确性.
  • 该框架在加速材料选方面表现出有效性.
  • 这种方法对学习复杂物质特性具有前途.

结论:

  • 与原子几何相比,电子结构描述器为学习复杂材料属性提供了更有前途的方法.
  • 开发的框架促进了高效的材料发现和选.
  • 这项工作推进了机器学习在预测光谱和内在材料特性方面的应用.