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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

1.4K
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

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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
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Machine learning accelerates Raman computations from molecular dynamics for materials science.

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
Summary
This summary is machine-generated.

Machine learning accelerates Raman spectroscopy calculations, enabling accurate predictions of molecular and material properties. This advancement makes Raman spectroscopy from molecular dynamics (MD-Raman) a more practical tool for scientific research.

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Area of Science:

  • Computational materials science
  • Spectroscopy
  • Theoretical chemistry

Background:

  • Raman spectroscopy is vital for characterizing molecules and materials.
  • First-principles calculations elucidate microscopic effects in Raman spectra.
  • Harmonic approximations miss crucial anharmonic vibrational effects and thermal changes.

Purpose of the Study:

  • To highlight the importance of theoretical treatments beyond harmonic phonons for Raman spectroscopy.
  • To discuss the role of anharmonic vibrational effects in materials.
  • To showcase recent advances in computational methods for Raman spectroscopy.

Main Methods:

  • Raman spectroscopy from molecular dynamics (MD-Raman) incorporates anharmonic vibrations and thermal effects.
  • Machine learning (ML) has dramatically accelerated MD-Raman computations.
  • The accuracy and predictive power of ML-accelerated methods are maintained.

Main Results:

  • Anharmonic effects are crucial for understanding Raman response in various materials.
  • MD-Raman computations, previously too expensive, are now practical.
  • ML significantly reduces the computational cost of MD-Raman.

Conclusions:

  • Recent ML advances make MD-Raman a powerful and practical tool for theoretical predictions.
  • MD-Raman and related methods are increasingly important for materials characterization.
  • The integration of ML enhances the utility of computational spectroscopy.