Representation-space diffusion models for generating periodic materials
Anshuman Sinha, Shuyi Jia, Victor Fung
TL;DR
This paper introduces a diffusion-based generative model that creates periodic material structures in a representation space, respecting physical symmetries and invariances, and reconstructs Cartesian structures via optimization, improving generation quality and simplicity.
Contribution
The novel approach generates materials directly in a physics-based descriptor space, ensuring invariance and symmetry considerations, with an efficient reconstruction method for Cartesian structures.
Findings
Competitive performance on benchmark datasets
Effective handling of periodic boundary conditions
Simplified training process compared to existing methods
Abstract
Generative models hold the promise of significantly expediting the materials design process when compared to traditional human-guided or rule-based methodologies. However, effectively generating high-quality periodic structures of materials on limited but diverse datasets remains an ongoing challenge. Here we propose a novel approach for periodic structure generation which fully respect the intrinsic symmetries, periodicity, and invariances of the structure space. Namely, we utilize differentiable, physics-based, structural descriptors which can describe periodic systems and satisfy the necessary invariances, in conjunction with a denoising diffusion model which generates new materials within this descriptor or representation space. Reconstruction is then performed on these representations using gradient-based optimization to recover the corresponding Cartesian positions of the crystal…
Peer Reviews
Decision·Submitted to ICLR 2024
- **Originality**: Several novel ideas are proposed by this work, including generating materials in the form of symmetry-invariant representations and searching atom coordinates to match embedded atom density representations. - **Quality**: Generally, the key points of the proposed method are clearly described, and the proof that the used material representations are invariant to rotation, translation, and periodic transformations is given in the appendix. Experiments on benchmark datasets sho
- It is not clear how the number of atoms $n$ is obtained from $C_{comp}$ as $C_{comp}$ is normalized by $n$. It is possible that materials with different $n$ may have the same $C_{comp}$. For instance, in $C_{comp}$ of materials with only carbon atoms, only the item for carbon atom is 1 while all other items are zeros, but there may exist different number of atoms $n$ in a unit cell. Authors are encouraged to clarify how $n$ is obtained. - There lack many significant details in the presentation
The idea of dealing with symmetries by learning in a controllable deterministic invariant space is nice (albeit not novel, see below). The paper clearly outlines the main ideas. The implementation seems well done.
It seems pretty clear this paper is far from finished. There's an idea, there's some early results, but this really just needs more work. It's impossible to tell at this stage if this idea work (it may, we just cannot tell). In particular, there are two key weaknesses - The evaluations are not very meaningful. The paper sets out to improve materials discovery through generative modeling. The goal of materials discovery is to find materials that are a) novel, b) thermodynamically stable, c) hav
- Generating periodic materials while preserving their structures is essential for accurately capturing and reconstructing the material's structural properties. The authors address this challenge by integrating representation embedding and the diffusion process. The way is straightforward and powerful. - The manuscript provides a comprehensive description of the model algorithms and experimental methods, ensuring a clear understanding of their model's computations.
- In the sections of Introduction and Related Works, the generation methods are discussed in terms of representation-based generation and direct material generation. However, when the authors describe the baseline models in the Results section, the two categories are not explicitly mentioned, leading to ambiguity regarding the categorization of these models. It appears that the authors exclusively compared their methods with those of direct material generation. This raises questions about the ex
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Taxonomy
TopicsAdvanced Mathematical Modeling in Engineering · Material Properties and Applications