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Conical Intersection Optimization Using Composed Steps Inside the ONIOM(QM:MM) Scheme: CASSCF:UFF Implementation with

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This summary is machine-generated.

Three algorithms optimize minimum energy conical intersections (MECI) for large molecules using ONIOM(QM:MM) and microiterations. The composed gradient-composed steps (CG-CS) method proved most efficient for studying excited-state processes in complex chemical systems.

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Area of Science:

  • Computational Chemistry
  • Theoretical Chemistry
  • Quantum Chemistry

Background:

  • Conical intersections are crucial for understanding excited-state dynamics in chemistry.
  • Efficient methods are needed to study these phenomena in large, complex molecules.

Purpose of the Study:

  • To implement and test three algorithms for optimizing minimum energy conical intersections (MECI) within an ONIOM(QM:MM) framework.
  • To assess the efficiency and applicability of these algorithms for large molecular systems.

Main Methods:

  • Implementation of composed gradient (CG), composed gradient-composed steps (CG-CS), and double Newton-Raphson-composed step (DNR-CS) algorithms.
  • Utilized ONIOM(QM:MM) scheme with CASSCF (QM) and UFF (MM) methods, incorporating microiterations.
  • Tested on 14 large hydrocarbons and diphenyl dibenzofulvene (DPDBF) in its crystal.

Main Results:

  • Microiterations enabled MECI optimization of large molecules with computational cost similar to smaller systems.
  • All three algorithms showed comparable performance, with CG-CS being the most efficient on average.
  • Successfully located the MECI of DPDBF in its crystal, confirming its role in aggregation-induced emission (AIE).

Conclusions:

  • The developed ONIOM(QM:MM) approach with microiterations is effective for optimizing MECIs in large molecules.
  • The study confirms that crystal packing hinders substituent rotation in DPDBF, preventing radiationless decay and leading to AIE.
  • The methodology is applicable to ONIOM(QM:QM) schemes and large-scale systems relevant to photochemistry and materials science.