Dis-GEN: Disordered crystal structure generation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918103273701376 |
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| author | Petersen, Martin Hoffmann Zhu, Ruiming Dai, Haiwen Aggarwal, Savyasanchi Wei, Nong Chen, Andy Paul Bhowmik, Arghya Lastra, Juan Maria Garcia Hippalgaonkar, Kedar |
| author_facet | Petersen, Martin Hoffmann Zhu, Ruiming Dai, Haiwen Aggarwal, Savyasanchi Wei, Nong Chen, Andy Paul Bhowmik, Arghya Lastra, Juan Maria Garcia Hippalgaonkar, Kedar |
| contents | A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a result, the physical and chemical properties of such materials are dependent on how the atomic species are distributed among the corresponding symmetrical sites, making them exceptionally challenging to model using computational methods. For this reason, existing generative models cannot handle the complexities of disordered inorganic crystals. To address this gap, we introduce Dis-GEN, a generative model based on an empirical equivariant representation, derived from theoretical crystallography methodology. Dis-GEN is capable of generating symmetry-consistent structures that accommodate both compositional disorder and vacancies. The model is uniquely trained on experimental structures from the Inorganic Crystal Structure Database (ICSD) - the world's largest database of identified inorganic crystal structures. We demonstrate that Dis-GEN can effectively generate disordered inorganic materials while preserving crystallographic symmetry throughout the generation process. This approach provides a critical check point for the systematic exploration and discovery of disordered functional materials, expanding the scope of generative modeling in materials science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dis-GEN: Disordered crystal structure generation Petersen, Martin Hoffmann Zhu, Ruiming Dai, Haiwen Aggarwal, Savyasanchi Wei, Nong Chen, Andy Paul Bhowmik, Arghya Lastra, Juan Maria Garcia Hippalgaonkar, Kedar Materials Science Disordered Systems and Neural Networks A wide range of synthesized crystalline inorganic materials exhibit compositional disorder, where multiple atomic species partially occupy the same crystallographic site. As a result, the physical and chemical properties of such materials are dependent on how the atomic species are distributed among the corresponding symmetrical sites, making them exceptionally challenging to model using computational methods. For this reason, existing generative models cannot handle the complexities of disordered inorganic crystals. To address this gap, we introduce Dis-GEN, a generative model based on an empirical equivariant representation, derived from theoretical crystallography methodology. Dis-GEN is capable of generating symmetry-consistent structures that accommodate both compositional disorder and vacancies. The model is uniquely trained on experimental structures from the Inorganic Crystal Structure Database (ICSD) - the world's largest database of identified inorganic crystal structures. We demonstrate that Dis-GEN can effectively generate disordered inorganic materials while preserving crystallographic symmetry throughout the generation process. This approach provides a critical check point for the systematic exploration and discovery of disordered functional materials, expanding the scope of generative modeling in materials science. |
| title | Dis-GEN: Disordered crystal structure generation |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2507.18275 |