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Multiplicative neural network potentials simulate cis-trans isomerization dynamics, revealing how environmental modes and vibronic coupling influence excited-state evolution and isomerization yield.

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Area of Science:

  • Quantum chemistry
  • Computational chemistry
  • Chemical dynamics

Background:

  • Conical intersections are crucial in nonadiabatic dynamics.
  • Environmental effects significantly impact molecular dynamics.
  • Accurate potential energy surfaces are needed for complex systems.

Purpose of the Study:

  • To employ multiplicative neural network (m-NN) potentials for simulating excited-state dynamics.
  • To investigate the interplay of vibronic effects and environmental modes.
  • To model cis-trans isomerization in protonated Schiff base systems.

Main Methods:

  • Multi-layer multi-configuration time-dependent Hartree (MCM-TDHF) simulations.
  • Use of m-NN potentials based on regularized diabatic states.
  • Inclusion of environmental effects via an overdamped Brownian oscillator model.
  • Application of the thermofield dynamics approach for thermal averages.

Main Results:

  • Vibronic effects and collective environmental modes act concertedly.
  • The environment's inertial timescale affects conical intersection approach and isomerization yield.
  • m-NN potentials accurately represent vibronic coupling beyond linear models.
  • Simulations show dynamics approaching a curved conical intersection seam followed by dissipation.

Conclusions:

  • The study demonstrates the capability of m-NN potentials for complex dynamics.
  • Environmental non-equilibrium evolution can be treated alongside intramolecular dynamics.
  • This work is a step towards including collective environmental effects in excited-state dynamics simulations.