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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

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Related Experiment Video

Updated: Jun 29, 2026

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
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Data-driven approach for benchmarking DFTB-approximate excited state methods.

Andrés I Bertoni1, Cristián G Sánchez1

  • 1Instituto Interdisciplinario de Ciencias Básicas (ICB-CONICET), Universidad Nacional de Cuyo, Padre Jorge Contreras 1300, Mendoza 5502, Argentina. csanchez@mendoza-conicet.gob.ar.

Physical Chemistry Chemical Physics : PCCP
|January 16, 2023
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Summary

This study benchmarks approximate density-functional tight-binding (DFTB) excited state (ES) methods using machine learning data. Findings reveal prediction errors strongly depend on chemical identity, offering insights for improving DFTB ES calculations.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • Approximate density-functional tight-binding (DFTB) methods are widely used for excited state (ES) calculations.
  • Existing DFTB ES methods have limitations due to approximations to density-functional theory (DFT).
  • Benchmarking these methods is crucial for understanding their accuracy and applicability.

Purpose of the Study:

  • To chemically benchmark approximate DFTB excited state (ES) methods within the DFTB+ suite.
  • To identify limitations of these methods by comparing them against more accurate reference data.
  • To provide recommendations for improving DFTB ES calculations.

Main Methods:

  • A chemically-informed, data-driven approach was employed.
  • The QM8 machine learning dataset, containing low-detail ES data, was utilized.
  • First singlet-singlet vertical excitation energies (E1) from approximate DFTB methods were compared to those from coupled cluster methods (CC2).

Main Results:

  • Clear trends in E1 prediction error distributions were identified across nearly 21,800 organic molecules (GDB-8 chemical space).
  • The accuracy of DFTB ES methods showed a strong dependence on the chemical identity of the molecules.
  • Valuable insights into the approximations made in DFTB ES methods were extracted.

Conclusions:

  • The study highlights the strengths and weaknesses of current approximate DFTB ES methods.
  • Recommendations for overcoming limitations and improving the accuracy of DFTB ES calculations are provided.
  • The findings contribute to the reliable application of DFTB methods in computational chemistry.