Differentiable MD has two main drawbacks. Firstly, the memory used in the optimization scales linearly with both the number of particles and the number of simulation steps due to the requirement of tracing all the operations in the MD algorithm. For a single 20 ps differentiable MD simulation, the memory allocated for the backpropagation of the gradients can reach ∼ 9 GB per task. Secondly, increasing the number of steps risks the so-called gradient explosion, where gradient sums in Eq. 13 grow instantly larger, leading to disruptive numerical instability. However, an adequate number of frames is needed to obtain a wellsampled property. To the best of our knowledge, only one work in the literature suggests an approach, using trajectory reweighing via Boltzmann distributions, that tries to alleviate the second problem. In this work, we opt for a parallel setup, as schematized in Fig. .