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Related Concept Videos

Deactivation Processes: Jablonski Diagram01:25

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Luminescence, the emission of light by a substance that has absorbed energy, is a process that involves the interaction of molecules with light. The energy-level diagram, or Jablonski diagram, is a graphical representation of these interactions, illustrating the various states and transitions a molecule can undergo. In a typical Jablonski diagram, the lowest horizontal line represents the ground-state energy of the molecule, which is usually a singlet state. This state represents the energies...
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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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Electrocyclic reactions are reversible reactions. They involve an intramolecular cyclization or ring-opening of a conjugated polyene. Shown below are two examples of electrocyclic reactions. In the first reaction, the formation of the cyclic product is favored. In contrast, in the second reaction, ring-opening is favored due to the high ring strain associated with cyclobutene formation.
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Related Experiment Video

Updated: Jan 14, 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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Machine Learning-Based Excited-State Reactive Force Field: A New Approach for Modeling the Photodissociation Dynamics

Xinyu Huang1, Zhongjun Zhou1, Huajie Song2

  • 1Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Changchun 130023, China.

Journal of Chemical Theory and Computation
|October 18, 2025
PubMed
Summary

Researchers developed a machine learning-based excited-state reactive force field (ML-ES-RFF) to accurately and efficiently model photochemical dynamics. This innovative approach overcomes limitations in studying complex molecular systems.

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Area of Science:

  • Computational Chemistry
  • Photochemistry
  • Machine Learning

Background:

  • Accurate and efficient excited-state reactive force fields are crucial for advancing photochemical dynamics research.
  • Current methods face limitations in balancing accuracy and computational cost for complex systems.

Purpose of the Study:

  • To develop a novel machine learning-based excited-state reactive force field (ML-ES-RFF).
  • To overcome the bottleneck in simulating excited-state dynamics by enhancing accuracy and efficiency.

Main Methods:

  • Implementation of an innovative divide-and-conquer strategy to classify degrees of freedom (DOFs).
  • Utilizing high-level quantum mechanics-based neural network potentials (QM-NNPs) for active DOFs.
  • Employing conventional molecular mechanics (MM) force fields for inactive DOFs, creating a multiscale methodology.

Main Results:

  • The ML-ES-RFF achieves both quantum chemical accuracy and remarkable computational efficiency.
  • Successfully applied to study the nonadiabatic dynamics of 2-fluorothiophenol.
  • Obtained complete atomistic characterization of the photodissociation process.

Conclusions:

  • The developed ML-ES-RFF provides a new paradigm for studying excited-state processes.
  • Enables accurate and efficient simulations of complex molecular systems.
  • Advances the field of photochemical dynamics research.