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On the accuracy of the LSC-IVR approach for excitation energy transfer in molecular aggregates.

Hung-Hsuan TehYuan-Chung Cheng
Published in: The Journal of chemical physics (2018)
We investigate the applicability of the linearized semiclassical initial value representation (LSC-IVR) method to excitation energy transfer (EET) problems in molecular aggregates by simulating the EET dynamics of a dimer model in a wide range of parameter regime and comparing the results to those obtained from a numerically exact method. It is found that the LSC-IVR approach yields accurate population relaxation rates and decoherence rates in a broad parameter regime. However, the classical approximation imposed by the LSC-IVR method does not satisfy the detailed balance condition, generally leading to incorrect equilibrium populations. Based on this observation, we propose a post-processing algorithm to solve the long time equilibrium problem and demonstrate that this long-time correction method successfully removed the deviations from exact results for the LSC-IVR method in all of the regimes studied in this work. Finally, we apply the LSC-IVR method to simulate EET dynamics in the photosynthetic Fenna-Matthews-Olson complex system, demonstrating that the LSC-IVR method with long-time correction provides excellent description of coherent EET dynamics in this typical photosynthetic pigment-protein complex.
Keyphrases
  • energy transfer
  • quantum dots
  • machine learning
  • molecular dynamics
  • molecular dynamics simulations
  • deep learning
  • high resolution
  • single molecule
  • amino acid
  • binding protein