Atomistic Generative Diffusion for Materials Modeling
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915496447705088 |
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| author | Rønne, Nikolaj Hammer, Bjørk |
| author_facet | Rønne, Nikolaj Hammer, Bjørk |
| contents | We present a generative modeling framework for atomistic systems that combines score-based diffusion for atomic positions with a novel continuous-time discrete diffusion process for atomic types. This approach enables flexible and physically grounded generation of atomic structures across chemical and structural domains. Applied to metallic clusters and two-dimensional materials using the QCD and C2DB datasets, our models achieve strong performance in fidelity and diversity, evaluated using precision-recall metrics against synthetic baselines. We demonstrate atomic type interpolation for generating bimetallic clusters beyond the training distribution, and use classifier-free guidance to steer sampling toward specific crystallographic symmetries in two-dimensional materials. These capabilities are implemented in Atomistic Generative Diffusion (AGeDi), an open-source, extensible software package for atomistic generative diffusion modeling. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_18314 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Atomistic Generative Diffusion for Materials Modeling Rønne, Nikolaj Hammer, Bjørk Computational Physics We present a generative modeling framework for atomistic systems that combines score-based diffusion for atomic positions with a novel continuous-time discrete diffusion process for atomic types. This approach enables flexible and physically grounded generation of atomic structures across chemical and structural domains. Applied to metallic clusters and two-dimensional materials using the QCD and C2DB datasets, our models achieve strong performance in fidelity and diversity, evaluated using precision-recall metrics against synthetic baselines. We demonstrate atomic type interpolation for generating bimetallic clusters beyond the training distribution, and use classifier-free guidance to steer sampling toward specific crystallographic symmetries in two-dimensional materials. These capabilities are implemented in Atomistic Generative Diffusion (AGeDi), an open-source, extensible software package for atomistic generative diffusion modeling. |
| title | Atomistic Generative Diffusion for Materials Modeling |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2507.18314 |