# Multi-timescale time encoding for CNN prediction of Fenna-Matthews-Olson energy-transfer dynamics

arXiv: https://arxiv.org/abs/2503.17430 · HTML: https://mlsanity.com/paper/2503.17430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 30 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Abstract (arXiv)

Machine learning simulations of open quantum dynamics often rely on recursive predictors that accumulate error. We develop a non-recursive convolutional neural networks (CNNs) that maps system parameters and a redundant time encoding directly to excitation-energy-transfer populations in the Fenna-Matthews-Olson complex. The encoding-modified logistic plus tanh functions-normalizes time and resolves fast, transitional, and quasi-steady regimes, while physics-informed labels enforce population conservation and inter-site consistency. Trained only on 0~ 7 ps reference trajectories generated with a Lindblad model in QuTiP, the network accurately predicts 0~100 ps dynamics across a range of reorganization energies, bath rates, and temperatures. Beyond 20 ps, the absolute relative error remains below 0.05, demonstrating stable long-time extrapolation. By avoiding step-by-step recursion, the method suppresses error accumulation and generalizes across timescales. These results show that redundant time encoding enables data-efficient inference of long-time quantum dissipative dynamics in realistic pigment-protein complexes, and may aid the data-driven design of light-harvesting materials.