2025/03/21 by Zhao, Shun-Cai, Huang, Yi-Meng, Yang, Yi-Fan +1
#2020: 05C70 #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Physics (quant-ph)
paper · doi:10.48550/arxiv.2503.17430
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.