Summary
This paper introduces a diffusion-based method for material generation. To adapt to the typical diffusion framework such as DDPM, the authors propose a new crystal representation, claiming to represent any crystal structures, for compatibility with UNet inputs. This representation integrates the locations of atoms within the crystal as additional dimensions of the element table, resulting in an image-like tensor analogous to those used in image-based diffusion models. In addition to applying existing metrics for evaluating material generation, the authors also propose a new evaluation approach using DFT to evaluate the physical validity of the generated materials. Experiments comparing a set of existing methods together with conditional generation are conducted to demonstrate the effectiveness of the proposed method.
Strengths
+ The paper presents a solid and novel technical contribution. The methodology distinctively diverges from existing diffusion-based material generative models by introducing a novel representation, more attuned to image-based diffusion models, which has been extensively explored in existing literature.
+ A notable strength of this paper lies in the introduction of a new evaluative metric designed to assess the physical validity of generated materials. The emphasis on the synthesizability of material generation is a refreshing approach, addressing an area that has seen limited exploration within the community. Using the DFT relaxation method from material science enhances the practical applicability and utility of the material generation method, providing deeper insights into its effectiveness and practicality.
+ The experimental outcomes presented in the paper are quite promising. The proposed method outperforms existing models by significant margins in most evaluated cases.
Weaknesses
- The authors claim that the proposed representation is capable of capturing any crystal structure, as well as being "scalable and flexible." While the completeness of the representation is clear and well-articulated, there appears to be a lack of detailed discussion regarding its redundancy or compactness. It would be beneficial if the authors could provide further explanations or experimental insights that illuminate how compact or redundant this representation might be in practice.
- In the section discussing conditional generation, the use of conditioning variables seems somewhat unclear. Specifically, on Page 4 under "Conditioned Diffusion with UniMat," the conditioning variables are directly concatenated with the noisy material along the last dimension. However, this approach raises questions as there appears to be a disparity in the feature spaces of the conditioning variables and the noisy material. For reference, in image conditional generation, cross-attention modules are commonly used to align the input condition (like text) with the image space effectively. Without the incorporation of a similar module in this work, the mechanism by which the conditioning variables guide the generation process remains unclear.
- Regarding the method's performance, it is noted that the proposed method does not attain 100% validity on larger datasets like MP20, in contrast to simpler approaches such as CDVAE. It would enhance the paper if the authors could delve deeper into this issue, offering more insights or explanations. Including potential solutions or future directions in addressing this limitation in the limitation section would also be quite valuable.
Questions
- In Figure 1, it might be beneficial to improve clarity by adding more descriptions or labels to the element table. It actually takes me some time to decipher that it represents an element table. Providing a more explicit explanation regarding the motivation and benefits of utilizing the element table for representation would also be advantageous. It seems that one evident benefit is the shared similarities in properties among neighboring elements in the table, potentially providing a useful prior for generation. Incorporating such observations and expanding on the motivations in the Introduction section would also be helpful.
- In Section 2.2, it could be helpful to include references to DDPM and extend the discussion slightly to incorporate considerations of other diffusion models, elaborating on why DDPM was the chosen approach. The discussion doesn’t need to be overly complicated: a straightforward explanation, such as the effective performance of DDPM in the authors’ use case, accompanied by some contextual background, would enhance the readers' understanding.
- In the section "Conditioned Diffusion with UniMat" on Page 4, the statement "While the unconditional ... training distribution" could be refined for precision and accuracy. It might be more accurate to state that DDPM primarily learns the score function rather than directly learning the training distribution, making it challenging to quantify the extent of overlap between the learned and training distributions.
- The section "Drawbacks of Learning Based Evaluations" in Section 2.3 is quite motivating. However, it might be more seamlessly integrated by briefly mentioning its main points in the Introduction. This could help prepare the reader for the detailed discussion that follows in Section 2.3.
- On Page 5 the reference format is wrong at the end of the second paragraph.
Rating
6: marginally above the acceptance threshold
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.