reviews: 
Comments: The paper proposed Dynamics Diffusion which is based on the denoising diffusion probabilistic model to enable the generation of molecular dynamics simulation trajectories of molecules and the sampling of rare events in the MD simulation. The Dyanmics Diffusion is used to train a UNet-based model which takes in the 3D coordinates of atoms and outputs a single value of conformation energy, of which the Jacobian can be calculated as the predicted noise. The model can also be conditioned on extra parameters such as the simulation temperature to enable conditional generation. To achieve rare event sampling of MD simulation trajectories, the authors adopted the methods of impainting and variation generation that condition the model on specific frames of the trajectories. Experiments have been done using a self-created dataset of particle Brownian motion in double-well and the public alanine dipeptide MD trajectory. Ablation studies of this paper focuses on the UNet architecture (encoder only, self-attention, etc.)
Strengths: 1. Despite some prior work in using diffusion model to train neural networks for the purpose of sampling MD trajectories, application of such method in rare-event sampling in low probability density region is still an under-research field. The impainting and variation generation methods used in this work are very interesting ideas which are demonstrated to work in the paper. 

2. The formulation of the Dynamics Diffusion that predicts the total energy of each frame is similar to methods used in many machine learning potential works. The forces on atoms can be then calculated as the gradient of energy by taking the Jacobian. Such a method ensure the physical validity between the force on atoms and the energy of system.
Weaknesses: 1. The transferability of the proposed UNet architecture can be problematic. The UNet takes a vector with the dimension of $3N$ as input and outputs a scaler (sum of energy of all $N$ atoms). The number of atoms is fixed and there is no information about the atom type. Such design means that the model after training is dedicated to a specific molecule without the possibility of being transferred to another system. It also means that for each molecule, a dataset of molecular dynamics simulation trajectories has to be curated for model training. Although the model can still be used for rare-event sampling as the probability of rare-events can be too low to be caught in MD simulation, the value in generating full MD simulation trajectory using such a model is low. 

2. The number of system tested in this paper is limited. The particle in double-well and alanine dipeptide are relatively small systems. Large systems such as protein dynamics (www.science.org/doi/10.1126/science.1208351) can be added to further strength the claim in this work.

3. The presentation of this work has plenty of room for improvement. The resolution of figures are low (especially Fig. 8 and 11). Labels of both axes in Fig. 11 are illegible. Model related information (architecture, optimizer etc) for both double-well and alanine dipeptide systems are highly redundant. No parentheses or brackets are used for reference in the manuscript. Reading the manuscript with reference author names randomly seperating the sentence is a great experience. When referring to a figure or table, the word "Figure" or "Table" is sometimes missing. Latex render error occurs in page 18 "Result" section. 

4. Some related works of learning MD trajectories using diffusion models (arxiv.org/abs/2305.18046) and rare event sampling using differentiable simulation (arxiv.org/abs/2301.03480) are missing from literature review.
Recommendation: 3: reject, not good enough
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 2 fair
Clarity: 1 poor