WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909833502916608 |
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| author | Kelvinius, Filip Ekström Andersson, Oskar B. Parackal, Abhijith S. Qian, Dong Armiento, Rickard Lindsten, Fredrik |
| author_facet | Kelvinius, Filip Ekström Andersson, Oskar B. Parackal, Abhijith S. Qian, Dong Armiento, Rickard Lindsten, Fredrik |
| contents | Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its position or element. We propose a generative model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based descriptions of crystals. This is enabled by considering a crystal structure representation that encodes all symmetry, and we design a novel neural network architecture which enables using this representation inside a discrete generative model framework. In addition to respecting symmetry by construction, the discrete nature of our model enables fast generation. We additionally present a new metric, Fréchet Wrenformer Distance, which captures the symmetry aspects of the materials generated, and we benchmark WyckoffDiff against recently proposed generative models for crystal generation. As a proof-of-concept study, we use WyckoffDiff to find new materials below the convex hull of thermodynamical stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06485 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry Kelvinius, Filip Ekström Andersson, Oskar B. Parackal, Abhijith S. Qian, Dong Armiento, Rickard Lindsten, Fredrik Materials Science Artificial Intelligence Machine Learning Crystalline materials often exhibit a high level of symmetry. However, most generative models do not account for symmetry, but rather model each atom without any constraints on its position or element. We propose a generative model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based descriptions of crystals. This is enabled by considering a crystal structure representation that encodes all symmetry, and we design a novel neural network architecture which enables using this representation inside a discrete generative model framework. In addition to respecting symmetry by construction, the discrete nature of our model enables fast generation. We additionally present a new metric, Fréchet Wrenformer Distance, which captures the symmetry aspects of the materials generated, and we benchmark WyckoffDiff against recently proposed generative models for crystal generation. As a proof-of-concept study, we use WyckoffDiff to find new materials below the convex hull of thermodynamical stability. |
| title | WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry |
| topic | Materials Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.06485 |