Paper

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

Shun-Cai, Zhao, Yi-Meng, Huang, Yi-Fan, Yang, Zi-Ran Zhao

2025

Overview

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 \\(\\) 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 \\(0100 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.

• Publisher: Cornell University Library, arXiv.org
• Subject: Artificial neural networks / Coding / Dynamics / Errors / Machine learning