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UV–Vis Spectroscopy: Molecular Electronic Transitions01:16

UV–Vis Spectroscopy: Molecular Electronic Transitions

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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...
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An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...
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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0,...
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Molecules possess discrete energy levels called quantum states. Unlike atoms, which have simpler energy levels, molecules possess additional rotational and vibrational energy levels.  Each energy level is separated by an energy gap, with the gaps between adjacent electronic, vibrational, and rotational levels varying significantly. The three types of energy levels in a diatomic molecule are shown in Figure 1.
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Electrolytes: van't Hoff Factor03:08

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Colligative Properties of Electrolytes
The colligative properties of a solution depend only on the number, not on the identity, of solute species dissolved. The concentration terms in the equations for various colligative properties (freezing point depression, boiling point elevation, osmotic pressure) pertain to all solute species present in the solution. Nonelectrolytes dissolve physically without dissociation or any other accompanying process. Each molecule that dissolves yields one...
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Updated: Jun 4, 2025

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
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Predicting electronic screening for fast Koopmans spectral functional calculations.

Yannick Schubert1, Sandra Luber1, Nicola Marzari2,3

  • 1Department of Chemistry, University of Zurich, 8057 Zurich, Switzerland.

Npj Computational Materials
|December 23, 2024
PubMed
Summary
This summary is machine-generated.

A new machine-learning model predicts screening parameters for Koopmans spectral functionals, significantly reducing computation time. This breakthrough enables accurate spectral property predictions for previously intractable problems.

Keywords:
Computational methodsElectronic properties and materialsElectronic structure

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Area of Science:

  • Computational chemistry
  • Quantum mechanics
  • Materials science

Background:

  • Koopmans spectral functionals offer high accuracy for predicting electronic spectral properties.
  • These functionals require computationally expensive screening parameters, limiting their application.
  • Current methods necessitate calculating these parameters for each new system.

Purpose of the Study:

  • To develop a computationally efficient machine-learning model for predicting Koopmans screening parameters.
  • To enable wider application of accurate Koopmans spectral functionals.
  • To reduce the computational cost associated with predicting spectral properties.

Main Methods:

  • Developed a machine-learning model to predict orbital-dependent screening parameters.
  • Input for the model: orbital densities from standard density-functional theory (DFT) calculations.
  • Validated the model using two prototypical systems.

Main Results:

  • The machine-learning model accurately predicts screening parameters with minimal training.
  • Orbital energies obtained using predicted parameters deviate by less than 20 meV on average compared to linear-response calculations.
  • Significant reduction in computational run times achieved.

Conclusions:

  • The developed machine-learning approach drastically cuts down computation time for Koopmans spectral functionals with minimal accuracy loss.
  • This method opens doors for applying these functionals to complex problems, including temperature-dependent spectral properties.
  • Combines frozen-orbital approximations and machine learning to efficiently measure energy-occupancy curvature.