The choice between fewer, well-sampled configurations or a broader exploration of conformations should align with your processes of interest and available compute. by reducing bias introduced by a single velocity distribution. Larger trajectories will have larger Lyapunov exponents [citation], and so it is critical to ensure that as many trajectories sample as large a configurational space as possible by varying the starting velocities. This helps ensure that even if the structural starting points are identical, the maximum possible phase space is explored. However, similar to running multiple configurations, a tradeoff exists between increased computational cost and improved sampling. For datasets focusing on understanding kinetic processes or identifying rates or intermediates