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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

2.6K
A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
2.6K
UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

27.8K
UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given structure by adding the...
27.8K
UV–Vis Spectroscopy: Molecular Electronic Transitions01:16

UV–Vis Spectroscopy: Molecular Electronic Transitions

2.5K
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...
2.5K
IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

4.2K
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...
4.2K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.6K
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...
1.6K
Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

1.2K
In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
1.2K

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Related Experiment Video

Updated: Dec 22, 2025

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
08:54

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Published on: January 25, 2020

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Machine learning approach for describing vibrational solvatochromism.

Kijeong Kwac1, Minhaeng Cho1

  • 1Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science (IBS), Seoul 02841, South Korea.

The Journal of Chemical Physics
|May 10, 2020
PubMed
Summary

Machine learning models, including neural networks, accurately predict vibrational frequency shifts in N-methylacetamide (NMA) solvated in water, outperforming previous methods. Polynomial functions proved superior to atom-centered symmetry functions as descriptors for this solvatochromism.

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Area of Science:

  • Computational chemistry
  • Machine learning applications
  • Spectroscopy

Background:

  • Machine learning (ML) is increasingly utilized in condensed matter physics.
  • Understanding molecular solvation effects on vibrational spectra is crucial.
  • N-methylacetamide (NMA) in water serves as a model system for studying solvation dynamics.

Purpose of the Study:

  • To apply feed-forward and convolutional neural networks to model vibrational frequency shifts.
  • To compare ML approaches with traditional methods like differential evolution algorithms.
  • To evaluate different descriptor types (ACSFs vs. polynomial functions) for solvation modeling.

Main Methods:

  • Utilized feed-forward and convolutional neural networks for vibrational analysis.
  • Trained models on a dataset of NMA configurations solvated in water.
  • Compared performance using atom-centered symmetry functions (ACSFs) and polynomial functions as input descriptors.

Main Results:

  • Neural network approaches achieved comparable or improved accuracy in predicting vibrational solvatochromic shifts.
  • Polynomial functions demonstrated superior performance over ACSFs in describing the amide I vibrational shifts.
  • The ML models effectively captured the frequency shifts of the amide I mode.

Conclusions:

  • Neural networks offer a powerful and accurate tool for describing vibrational solvatochromism in condensed matter systems.
  • Simple polynomial functions are effective descriptors for modeling solvation effects on molecular vibrations.
  • This study highlights the potential of ML in advancing spectroscopic analysis and understanding molecular interactions.