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Multi-Time-Scale Time Encoding for CNN Prediction of Fenna-Matthews-Olson Energy-Transfer Dynamics.

Shun-Cai Zhao1, Yi-Meng Huang1, Yi-Fan Yang1

  • 1Center for Quantum Materials and Computational Condensed Matter Physics, Kunming University of Science and Technology, Kunming 650500, P. R. China.

The Journal of Physical Chemistry Letters
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

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We developed nonrecursive convolutional neural networks (CNNs) for simulating quantum dynamics, significantly reducing error accumulation. This method accurately predicts long-time excitation energy transfer (EET) in pigment-protein complexes.

Area of Science:

  • Quantum Dynamics
  • Machine Learning
  • Spectroscopy

Background:

  • Simulating open quantum dynamics often uses recursive methods prone to error accumulation.
  • Accurate long-time predictions are crucial for understanding energy transfer in systems like the Fenna-Matthews-Olson (FMO) complex.

Purpose of the Study:

  • To develop a nonrecursive machine learning model for accurate, long-time simulations of quantum dynamics.
  • To predict excitation energy transfer (EET) populations in the FMO complex without accumulating errors.

Main Methods:

  • Developed nonrecursive convolutional neural networks (CNNs) mapping system parameters and time encoding to EET populations.
  • Utilized a novel time encoding function and physics-informed labels for population conservation.
  • Trained the CNN on short-time (0-7 ps) reference trajectories from a Lindblad model.

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Main Results:

  • The CNN accurately predicts EET dynamics from 0 to 100 ps across various parameters (reorganization energies, bath rates, temperatures).
  • Achieved low absolute relative error (<0.05) beyond 20 ps, demonstrating stable long-time extrapolation.
  • Suppressed error accumulation compared to recursive methods, generalizing across time scales.

Conclusions:

  • Nonrecursive CNNs with redundant time encoding enable data-efficient inference of long-time quantum dissipative dynamics.
  • This approach is effective for realistic pigment-protein complexes and can aid in designing light-harvesting materials.