reviews: 
Comments: The authors present MOFDiff, a diffusion model that generates a coarse-grained (CG) representation of metal-organic framework (MOF) structures using a diffusion model. MOFs are represented hierarchically with sets of atoms grouped to represent building blocks. Each MOF is represented with K building blocks such that K << N, where N is the number of atoms in the periodic cell. This is particularly important as typical MOF unit cells may contain on the order of 100s of atoms. The authors first embed the building blocks using a model trained on contrastive learning loss. The latent vector is then used to condition a denoising diffusion model to generate CG representations of MOFs. CG represents are converted back to MOFs using a novel assembly algorithm. The authors define a validity criterion for their generated MOFs and show they are capable of generating valid, novel, diverse MOFs. MOFDiff is also capable of guided inverse design and optimizing MOFs CO2 separation for carbon capture.
Strengths: * The paper is easy to follow and the authors motivate their reasoning for model and design choices
* The contrastive representation learning of building blocks allows the authors to learn a meaningful latent space of building blocks including metal nodes and linkers. This extends previous work which only modified linkers and as a result, generated low-diversity samples
* The proposed assembly algorithm to orient multiple building blocks using gradient-based optimization is novel and provides a strong justification for the authors' data representation choices
Weaknesses: * In the generation process, it is unclear why the latent vector Z can be sampled from the standard normal distribution. The latent vector is generated using the OrbNet encoder trained with the contrastive learning loss. Without further regularization, like in the case of variational autoencoders using the KL divergence, the latent vector should not conform to the distribution.
* It would be useful to compare with previous work [1] on the inverse design of MOFs. The authors mention and cite a source on the low diversity of the generated MOFs with template-based systems such as SmVAE, but a comparison isn’t present in the experiments section.
* The validity and novelty of generated  structures are low compared to template-based models as in [1].
* In general, while the authors include relevant work in the paper, the authors should have a related work section to place their work in the context of the field.
    * The following papers could potentially be related to the present work [2], [3].

[1] Yao, Z., Sánchez-Lengeling, B., Bobbitt, N. S., Bucior, B. J., Kumar, S. G. H., Collins, S. P., ... & Aspuru-Guzik, A. (2021). Inverse design of nanoporous crystalline reticular materials with deep generative models. Nature Machine Intelligence, 3(1), 76-86.

[2] Zhou, M., & Wu, J. (2022). Inverse design of metal–organic frameworks for C2H4/C2H6 separation. npj Computational Materials, 8(1), 256.

[3] Park, Junkil, et al. "Computational design of metal–organic frameworks with unprecedented high hydrogen working capacity and high synthesizability." Chemistry of Materials 35.1 (2022): 9-16.
Recommendation: 8: accept, good paper
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 3 good