Abstract: Molecular dynamics simulations are fundamental tools for quantitative molecular sciences. However, these simulations are computationally demanding and often struggle to sample rare events crucial for understanding spontaneous organization and reconfiguration in complex systems. To improve general speed and the ability to sample rare events in a directed fashion, we propose a method called $\textit{DynamicsDiffusion}$ based on denoising diffusion probabilistic models (DDPM) to generate molecular dynamics trajectories from noise. The generative model can then serve as a surrogate to sample rare events. We leverage the properties of DDPMs, such as conditional generation, the ability to generate variations of trajectories, and those with certain conditions, such as crossing from one state to another, using the 'inpainting' property of DDPMs, which became only applicable when generating whole trajectories and not just individual conformations. To our knowledge, this is the first deep generative modeling for generating molecular dynamics trajectories. We hope this work will motivate a new generation of generative modeling for the study of molecular dynamics.