Kati8/ICLR_2023_2025 huggingface dataset:
authors: Magnus Petersen
authors: Gemma Roig
authors: Roberto Covino
reviews: 
Comments: This paper considers the rare event sampling problem in the molecular dynamics simulation which aims to sample the transition state (with a high energy barrier) between two metastable states. Specifically, this paper proposes to solve this problem with a diffusion model that learns to simulate such trajectories (transition paths between the two metastable states). The authors leverage the inpainting flexibility of diffusion models to inpaint missing dynamics in the trajectories. Experiments are conducted on both a synthetic and a real-world molecular dynamics simulation problem.
Strengths: * Sampling rare events and transition paths is a crucial problem in computational chemistry and has wide applications in studying a wide range of chemical reactions such as protein folding, protein-ligand binding, etc. 
* The proposed method that uses the diffusion model to model the distributions of transition paths and sampling new transition path is technically sound.
Weaknesses: * The major concern of this work is both the generalizability and scalability of the proposed method. From the method description and experiment section, this method requires sampling a path (though seems to be short, 10 timesteps) as the training data. There is no evidence that demonstrates this could generalize to longer-time dynamics or the transition path (which even requires crossing the energy barrier) nor scale to larger systems since the size of the system scales with the number of the frames used (and I believe for more complicated systems, more frames are needed).
* It seems the different timestep of the path need to be properly isolated when running the diffusion models, otherwise they may affect each other. 
* This is not a major concern but the proposed method itself does not contribute to the technical novelty of this paper as it mostly uses diffusion models developed in previous work and deployed in this problem.
* I am also worried about how physical the generated paths would be. Even though the authors demonstrate the distribution plots and how the dihedral angels change, from my personal experience, sometimes the paths could completely fail while the dihedrals still look good. It would be good to see more evidence in terms of how physical the generated paths are or visualizing some paths.
* Some related work in this area is missing. [1] is also a directed generation approach to find the transition path between two metastable states.

[1] Holdijk, L., Du, Y., Hooft, F., Jaini, P., Ensing, B. and Welling, M., 2022. PIPS: Path Integral Stochastic Optimal Control for Path Sampling in Molecular Dynamics.
Recommendation: 3: reject, not good enough
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 3 good
reviews: 
Comments: In this work the authors present an extension of the standard application of diffusion models in the context of molecular systems to additionally study the dynamics of the systems. The authors achieve this by generating trajectories trained on MD simulation data instead of single instances of the system. The proposed method is evaluated using both a 2D toy example as well as small molecular system in the form of Alanine Dipeptide.
Strengths: **Originality:** The method lacks algorithmic novelty, most of the presented work are adapted from prior work on diffusion models for time series and molecular data, but this is alleviated by the originality the paper has in applying these previously established techniques to a novel application area. Using Diffusion models for generating molecular transitions is in itself an interesting and novel idea. 

**Significance:** The paper addresses and important problem in (computational) chemistry, the sampling of rare events.
Weaknesses: Unfortunately the paper lacks in quality and clarity. I've elaborated on this below in my detailed comments and requests for clarifications, but in general the papers notation is hard to follow and unfortunately the experiments do not address the claims made.
Recommendation: 3: reject, not good enough
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 1 poor
reviews: 
Comments: This paper proposes DynamicsDiffusion, a method using the denoising diffusion probabilistic model to simulate the trajectories of molecular dynamics.
Strengths: - This paper studies an important problem of MD simulation.
- The qualitative results are nice.
Weaknesses: Unfortunately, the key idea of this paper, using generative modeling (DDPM) for MD simulation, has been done in several works before [1, 2, 3]. They have been archived for over six months, and all have been accepted to relevant conferences/journals. The methods have almost no difference.

[1] https://pubs.acs.org/doi/10.1021/acs.jctc.3c00702

[2] https://dl.acm.org/doi/abs/10.1609/aaai.v37i4.25663

[3] https://openreview.net/forum?id=y8RZoPjEUl
Recommendation: 3: reject, not good enough
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 2 fair
Clarity: 2 fair
reviews: 
Comments: Based on DDPM, the author proposes a extension of dynamics diffusion to generate molecular dynamics. The model leverages the conditional generation property of DDPM to generate various trajectories. The model is the first deep generative model for molecular dynamics to the authors' knowledge.
Strengths: Originality: Combination of DDPM and molecular dynamics. The original part of DDPM is to introduce two variants of sampling. Moreover, a physics-inform layer is introduced to the Unet block. 

Quality: A lot of essential parts are missing.

Clarity: The writing is poor. Many typos and the weird reference style make it hard to read smoothly. There are also missing references in the appendix.

Significance: It is the first generative model for molecular dynamics. The approach enables rare events enhanced sampling. It can reconstruct free energy and dynamics accurately.
Weaknesses: The novelty seems straightforward compared with the original DDPM. The reference style looks weird. The writing is not organized. Some important details are missing. For example, what is the math expression of the energy formulation? The paper also misses many key results to support the claims. What is the performance difference between your proposed 2 sampling methods and the baseline method? Also, the authors claim the proposed model is suitable for the symmetries, but the reviewer didn't find the corresponding result to support it. There is no part to cite Figure 2, which makes the reviewer guess which part corresponds to Figure 2.
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: 3 good
Clarity: 1 poor