Input Target Figure : Transformation of the kth unsupervised trajectory of the reduced density matrix ρ(t) propagated with the traditional quantum dynamics method from initial time t 0 up to simulation time t M into one training point for the OSTL model. We include j as a label to distinguish between the initial excitation on site-1 and site-6. Simulation parameters λ, γ and T are the reorganization energy, characteristic frequency, and temperature, respectively. As ρ nm (t) = ρ * mn (t) (n ̸ = m), we consider only the upper diagonal terms ρ nm (t) (n ≤ m) and as the off-diagonal terms are complex, we learn the real and imaginary parts independently. validation loss (mean squared error of 2.57 • 10 -7 ) evaluated for 200 trajectories which make up ca. 5% of the whole data set (not included in the training set and chosen by the farthest point sampling). This best ML model was used to propagate 10 ps long-time dynamics for the test trajectories predicting the exciton populations and coherence for seven sites (Figs. and). The OSTL trajectories for the reduced density matrix ρ(t) agree very well with the reference traditional trajectories for both initial excitations on site-1 and site-6 demonstrating that OSTL approach can successfully learn the challenging quantum beating (modulation of amplitudes) in the excitation energy transfer, along with the off-diagonal terms representing the coherence (Figs. and show trajectories for one random set of simulation parameters, other trajectories are provided as described in the Data availability section). We compare our results to the reference LTLME method (dots). Dynamics of the corresponding off-diagonal terms are shown Fig. . In our calculations, γ and λ are considered in the units of cm -1 , while T is in the units of K.