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Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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Distribution of Molecular Speeds01:27

Distribution of Molecular Speeds

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The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...
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Updated: Jan 16, 2026

A Multimodal Wide-Field Fourier-Transform Raman Microscope
06:48

A Multimodal Wide-Field Fourier-Transform Raman Microscope

Published on: December 30, 2025

114

机器学习加速了从分子动力学到材料科学的拉曼计算.

David A Egger1,2, Manuel Grumet1,2, Tomáš Bučko3,4

  • 1Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany.

The Journal of chemical physics
|September 30, 2025
PubMed
概括
此摘要是机器生成的。

机器学习加速了拉曼光谱计算,使分子和材料属性的准确预测成为可能. 这一进步使得分子动力学的拉曼光谱 (MD-Raman) 成为科学研究中更实用的工具.

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Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems

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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
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Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems
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科学领域:

  • 计算材料科学 计算材料科学
  • 频谱学是一种光谱学.
  • 理论化学是一种理论化学.

背景情况:

  • 拉曼光谱对于描述分子和材料至关重要.
  • 第一原则计算阐明了拉曼光谱中的微观效应.
  • 波近似错过了关键的波振动效应和热变化.

研究的目的:

  • 为突出拉曼光谱学超越波子理论处理的重要性.
  • 讨论材料中无和的振动效应的作用.
  • 展示拉曼光谱计算方法的最新进展.

主要方法:

  • 从分子动力学的拉曼光谱学 (MD-Raman) 结合了无和的振动和热效应.
  • 机器学习 (ML) 极大地加速了MD-Raman计算.
  • 保持ML加速方法的准确性和预测能力.

主要成果:

  • 无声效应对于理解各种材料中的拉曼反应至关重要.
  • 以前过于昂贵的MD-Raman计算现在是实用的.
  • ML显著降低了MD-Raman的计算成本.

结论:

  • 最近的ML进步使MD-Raman成为理论预测的强大而实用的工具.
  • 对于材料的表征,MD-Raman和相关方法越来越重要.
  • 机器学习的整合增强了计算光谱学的实用性.