Kati8/ICLR_2023_2025 huggingface dataset:
authors: Xiang Fu
authors: Tian Xie
authors: Andrew Scott Rosen
authors: Tommi S. Jaakkola
authors: Jake Allen Smith
reviews: 
Comments: The paper presents a coarse grained diffusion model for generating metal organic frameworks. Coarse structures are generated by a diffusion model, after which 3d atomic structures are assembled, oriented using a heuristic optimization procedure, and optimized using a force field. The coarse grained building blocks are represented using a learned (contrastive) representation. The generative model is evaluated on an inverse design problem using GCMC simulation.
Strengths: For the most part, the paper is easy to follow. There are several illustrative figures that provide a good overview

The paper addresses a very important and difficult problem, and the proposed method is sensible and (at least to some degree) effective.

There are several technical novelties, including the particular way the coarse graining and reassembly is conducted.

The method is validated with GCMC simulation.
Weaknesses: There are many "moving parts" in the modeling pipeline. It would be enlightening with more ablation studies or comparisons of different modeling choices to yield some insight into the sensitivity to the different modeling choices.

Some technical details are not described in detail. The paper builds on several existing techniques, which are referenced but not described technically. This is just a minor point, but in my view the paper could perhaps be more self contained.
Recommendation: 8: accept, good paper
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: 3 good
reviews: 
Comments: The paper introduces a novel method for generating metal-organic frameworks (MOFs) using a coarse-grained diffusion model called MOFDiff. MOFDiff employs a coarse-grained (CG) representation derived from MOF building blocks. A graph neural network (GNN) encoder trained via contrastive learning is used to map these building blocks into a latent space (z). MOFDiff consists of four main components: 
1. A periodic contrastive GNN encoder for latent embedding
2. A MLP for lattice parameter (L) and building block (K) count prediction
3. A periodic GNN denoiser for diffusion-based MOF generation
4. Finally another MLP for MOF property prediction e.g., CO2 capacity. 

The denoising diffusion process consists of two steps, first on the CG based building block types and then on their 3D coordinates. Then the all atom structure is recovered using an assembly algorithm, followed by simple force field (UFF) based relaxation. MOFDiff is trained on a dataset of ~304k MOFs and shows capability in generating valid and diverse MOFs. Overall, this paper leverages a diffusion model over a CG representation of MOFs to efficiently generate complex new structures, with proven efficacy in carbon capture applications.
Strengths: 1. The CG representation of building blocks for the diffusion model makes MOFDiff computationally efficient to train and sample new MOFs. The all atom  

2. Underlying physical symmetries are appropriately handled with contrastively trained GNN encoder for the building blocks

3. Optimizing MOFs in the latent space rather than decoding repeatedly is an efficient inverse design strategy demonstrated in the paper.
Weaknesses: 1. As acknowledged in the paper, the validity of the MOFs decreases as number of building blocks, this could limit applicability to larger and more complex MOFs.

2. The authors use a MOF dataset with less than <20 building blocks. But the paper doesn't discuss about size extensivity of the GNN encoder and denoising module in detail.  

3. There is limited discussion on the synthetic accessibility of the designed MOFs. Finally, the building blocks are constrained to a certain set  to allow coarse graining, this may led to less diverse MOFs.
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: 3 good
Clarity: 3 good
reviews: 
Comments: The paper presents a method for generating Metal-Organic Framework (MOF) structures with target properties such as carbon capture.

The proposed method consists of a contrastive learning framework for embedding the MOF blocks based on their ECFP4 fingerprint. These representations are then used to work with a coarse-grained representation of the MOF. A latent variable model is then trained to generate a new MOF. Sampling from the latent variable allows for estimating the lattice structure and the number of building blocks. Then a diffusion-based decoder is used to produce the building block types and their location in the unit cell. The building blocks are then oriented and connections are established between them. Finally, a force field relaxation is computed to finalize the position and orientation of all atoms in the MOF. 

The MOFs are validated based on well-established domain knowledge. The quality of the generated MOF structures is assessed for key application tasks such as carbon capture using well-established Monte Carlo simulation. 

The proposal goes beyond the limitations of template-based methods and has computational advantages in proposing novel MOF structures with desired properties.
Strengths: The paper clearly demonstrates the strong technical background in Material Science of the authors. The focus is on achieving concrete results in MOF generation that can have an impact on key applications of MOF materials.  Correspondingly, the validation of the results is substantive. 

The selected Machine Learning components, such as the choice of the latent variable model, diffusion model, and contrastive learning are precisely selected to overcome specific challenges towards the goal of generating MOFs.
Weaknesses: The above-given strengths are closely related to the weaknesses of the paper. Clearly, a very good application paper that makes important advances for computational material science may have limited contribution to the field of Machine Learning (ML). 

While a strong contribution to Material Science, from the ML perspective the work is a good combination of existing methods but does not go much further than that. The paper tells a story about many of the design decisions that were taken, but many other choices could have been made with even more recent approaches. 

In that sense, the main weakness is the alignment to the ICLR conference.
Recommendation: 8: accept, good paper
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: 4 excellent