For the ZnPc-C 60 donor-acceptor system, modeled with 661 basis functions and 4 excited states, we show the job time for electronic structure contributions averaged across the trajectory in Figure . The calculation of the excited state gradient and the NAC vectors between excited states takes up the majority of the computation time. Because of the number of states used throughout the simulation, about 66% of the computational job time was spent computing the 10 NAC vectors. We note that for this trajectory, we see little participation from the S 0 state in the dynamics suggesting that we may be able to eliminate this state from future simulations of the dynamics, thereby reducing the number of NAC vectors to be computed. The total job time for a single LSC step averaged across the trajectory was 784s, but parallelization over two GPU nodes brought the average wall time per step to 399s, a performance gain of 196%. Like with the fulvene system in Figure that was also parallelized over two compute nodes, the first two excited state gradients, S 1 and S 2 , take the longest to compute as they are the first and only jobs that solve for the TDDFT excited state coefficients. However, because there are many more jobs after the S 1 and S 2 gradient calculations that use the excited state coefficients than in the fulvene system, the computational performance is nearly double that of a single node. We conclude with a reminder that the simulations reported here demonstrate the abilities of the PySCES code and document timings for a single trajectory; however, in an actual investigation of excited state dynamics, an ensemble of independent trajectories would be evolved to compute the corresponding LSC correlation function.