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Related Concept Videos

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

1.5K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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Applications of IR Spectroscopy: Overview01:11

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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...
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IR and UV–Vis Spectroscopy of Carboxylic Acids01:28

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In IR spectroscopy of carboxylic acids, the C=O bond shows a characteristic band between 1710 and 1760 cm⁻¹, and the O–H bond exhibits a broad band between 2500 and 3300 cm⁻¹.
However, the stretching absorptions for the C=O bond vary depending on the structure of carboxylic acids. The C=O bond of the free carboxylic acids shows a higher stretching frequency, 1760 cm−1, while H-bonded carboxylic acids (dimers) exhibit stretching absorptions at a lower frequency,...
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IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

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Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the...
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Spectroscopy of Carboxylic Acid Derivatives01:26

Spectroscopy of Carboxylic Acid Derivatives

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Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and...
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Machine learning now predicts molecular structures directly from Infrared (IR) spectra. This transformer model unlocks full spectral data, improving chemical analysis beyond human interpretation.

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Area of Science:

  • Computational chemistry
  • Analytical chemistry
  • Spectroscopy

Background:

  • Machine learning (ML) applications in chemistry are advancing rapidly.
  • Analytical chemistry has seen interest from ML, but practical adoption is limited.
  • Infrared (IR) spectroscopy is accessible but typically identifies only limited functional groups due to complex peak interpretation.

Purpose of the Study:

  • To develop a transformer model for direct molecular structure prediction from complete IR spectra.
  • To overcome the limitations of human interpretation in IR spectroscopy.
  • To leverage the full information content of IR spectra for chemical analysis.

Main Methods:

  • A transformer model was designed for IR spectral analysis.
  • The model was pre-trained on a large dataset of 634,585 simulated IR spectra.
  • Fine-tuning was performed using 3,453 experimental IR spectra.

Main Results:

  • The model achieved 44.4% top-1 and 69.8% top-10 accuracy for predicting molecular structures (6-13 heavy atoms).
  • For scaffold prediction, the model achieved 84.5% top-1 and 93.0% top-10 accuracy.
  • Demonstrated ability to utilize comprehensive spectral information.

Conclusions:

  • The transformer model effectively predicts molecular structures and scaffolds from IR spectra.
  • This approach significantly enhances the utility of IR spectroscopy in chemistry.
  • Enables deeper insights from spectral data beyond traditional functional group identification.