Generating Symmetric Materials using Latent Flow Matching
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913110809378816 |
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| author | Karmush, Anmar Brandenburg, Cedric Mathieu Ershadrad, Soheil Rosén, Johanna Felsberg, Michael Kelvinius, Filip Ekström |
| author_facet | Karmush, Anmar Brandenburg, Cedric Mathieu Ershadrad, Soheil Rosén, Johanna Felsberg, Michael Kelvinius, Filip Ekström |
| contents | Tackling the task of materials generation, we aim to enhance the previously proposed All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-aware variant. To do so, we use a representation of materials based on Wyckoff positions. We follow ADiT and perform generative modelling in latent space, adapted to our symmetry-aware representation. By forcing the output of the generative model to adhere to the symmetry restrictions imposed by the generated crystal's space group and each atom's Wyckoff-position, the generated materials exhibit more realistic symmetry properties. We benchmark our method against both symmetry-aware and symmetry-agnostic models for materials generation and show competitive performance, generating stable, symmetric materials with a simple Transformer architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10115 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generating Symmetric Materials using Latent Flow Matching Karmush, Anmar Brandenburg, Cedric Mathieu Ershadrad, Soheil Rosén, Johanna Felsberg, Michael Kelvinius, Filip Ekström Machine Learning Materials Science Tackling the task of materials generation, we aim to enhance the previously proposed All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-aware variant. To do so, we use a representation of materials based on Wyckoff positions. We follow ADiT and perform generative modelling in latent space, adapted to our symmetry-aware representation. By forcing the output of the generative model to adhere to the symmetry restrictions imposed by the generated crystal's space group and each atom's Wyckoff-position, the generated materials exhibit more realistic symmetry properties. We benchmark our method against both symmetry-aware and symmetry-agnostic models for materials generation and show competitive performance, generating stable, symmetric materials with a simple Transformer architecture. |
| title | Generating Symmetric Materials using Latent Flow Matching |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2605.10115 |