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A new adaptive algorithm efficiently finds minimum-energy three-states conical intersections (ME3CI). This method improves convergence and reveals more ME3CI geometries, suggesting their greater role in photochemistry.

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Area of Science:

  • Computational Chemistry
  • Theoretical Chemistry
  • Quantum Chemistry

Background:

  • Conical intersections (CIs) are crucial in photochemical reactions.
  • Minimum-energy conical intersections (MECIs) are key intermediates.
  • Three-state CIs are important but challenging to locate.

Purpose of the Study:

  • Introduce a novel adaptive penalty function algorithm for optimizing minimum-energy three-states conical intersections (ME3CI).
  • Enhance the efficiency and accuracy of locating ME3CI geometries.
  • Investigate the abundance and photochemical relevance of three-state CIs.

Main Methods:

  • Developed a new adaptive penalty function algorithm with modifications: target function redundancy removal, adaptive penalty weighting, and tighter energy gap convergence criteria.
  • Tested the algorithm on butadiene and malonaldehyde systems.
  • Employed the mixed-reference spin flip time-dependent density functional theory (MRSF-TDDFT) method.

Main Results:

  • Successfully recovered previously identified true ME3CI geometries in butadiene and malonaldehyde.
  • Identified seven new ME3CI geometries in butadiene.
  • Demonstrated improved convergence rate compared to the original penalty function algorithm.
  • Showed that a previously identified CI in butadiene was a narrowly avoided crossing.

Conclusions:

  • The new adaptive algorithm is more efficient for locating ME3CI.
  • Three-state conical intersections may be more prevalent in photochemistry than previously assumed.
  • MRSF-TDDFT provides accurate ME3CI geometries and energies at a lower computational cost.