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相关概念视频

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

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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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相关实验视频

Updated: Jan 7, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Mol2Raman:一个图形神经网络模型,用于从SMILES表示中预测Raman光谱.

Salvatore Sorrentino1,2,3, Alessandro Gussoni4, Francesco Calcagno5,6

  • 1Department of Physics, Politecnico di Milano Piazza Leonardo da Vinci, 32 20133 Milan Italy salvatore.sorrentino@polimi.it dario.polli@polimi.it.

Digital discovery
|December 12, 2025
PubMed
概括

一个深度学习框架Mol2Raman准确地从分子结构中预测Raman光谱. 这种计算工具通过提供快速,可靠的光谱预测来加速分子设计和材料发现.

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科学领域:

  • 计算化学计算化学
  • 频谱学是一种光谱学.
  • 机器学习 机器学习

背景情况:

  • 拉曼光谱对于分子分析至关重要,但在计算上很难预测.
  • 由于复杂的结构-频谱关系,对拉曼光谱的准确预测受到阻碍.

研究的目的:

  • 介绍Mol2Raman,这是一个深度学习框架,用于从SMILES字符串直接预测Raman光谱.
  • 能够准确预测不同分子结构的峰值位置和强度.

主要方法:

  • 使用边缘特征的图形同态网络 (GINE) 来编码分子拓.
  • 在超过31,000个DFT计算的拉曼光谱的数据集上训练模型.
  • 将Mol2Raman与基于指纹和Chemprop模型进行比较.

主要成果:

  • 在预测拉曼光谱方面,Mol2Raman实现了高准确性,超过了现有的方法.
  • 该模型准确地预测了结构不同分子和反体的光谱特征.
  • 展示快速推断时间 (22毫秒/分子),适合高通量选.

结论:

  • Mol2Raman为拉曼光谱预测提供了一个可扩展,准确和可解释的平台.
  • 开放式访问的Web应用程序可以在没有专门的硬件的情况下进行实时预测.
  • 这个框架推动了分子设计,材料发现和光谱诊断.