DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915512691195904 |
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| author | Pan, Elton Kwon, Soonhyoung Liu, Sulin Xie, Mingrou Hoffman, Alexander J. Duan, Yifei Prein, Thorben Sheriff, Killian Roman-Leshkov, Yuriy Moliner, Manuel Gomez-Bombarelli, Rafael Olivetti, Elsa |
| author_facet | Pan, Elton Kwon, Soonhyoung Liu, Sulin Xie, Mingrou Hoffman, Alexander J. Duan, Yifei Prein, Thorben Sheriff, Killian Roman-Leshkov, Yuriy Moliner, Manuel Gomez-Bombarelli, Rafael Olivetti, Elsa |
| contents | The synthesis of crystalline materials, such as zeolites, remains a significant challenge due to a high-dimensional synthesis space, intricate structure-synthesis relationships and time-consuming experiments. Considering the one-to-many relationship between structure and synthesis, we propose DiffSyn, a generative diffusion model trained on over 23,000 synthesis recipes spanning 50 years of literature. DiffSyn generates probable synthesis routes conditioned on a desired zeolite structure and an organic template. DiffSyn achieves state-of-the-art performance by capturing the multi-modal nature of structure-synthesis relationships. We apply DiffSyn to differentiate among competing phases and generate optimal synthesis routes. As a proof of concept, we synthesize a UFI material using DiffSyn-generated synthesis routes. These routes, rationalized by density functional theory binding energies, resulted in the successful synthesis of a UFI material with a high Si/Al$_{\text{ICP}}$ of 19.0, which is expected to improve thermal stability and is higher than that of any previously recorded. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17094 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning Pan, Elton Kwon, Soonhyoung Liu, Sulin Xie, Mingrou Hoffman, Alexander J. Duan, Yifei Prein, Thorben Sheriff, Killian Roman-Leshkov, Yuriy Moliner, Manuel Gomez-Bombarelli, Rafael Olivetti, Elsa Materials Science Artificial Intelligence Machine Learning The synthesis of crystalline materials, such as zeolites, remains a significant challenge due to a high-dimensional synthesis space, intricate structure-synthesis relationships and time-consuming experiments. Considering the one-to-many relationship between structure and synthesis, we propose DiffSyn, a generative diffusion model trained on over 23,000 synthesis recipes spanning 50 years of literature. DiffSyn generates probable synthesis routes conditioned on a desired zeolite structure and an organic template. DiffSyn achieves state-of-the-art performance by capturing the multi-modal nature of structure-synthesis relationships. We apply DiffSyn to differentiate among competing phases and generate optimal synthesis routes. As a proof of concept, we synthesize a UFI material using DiffSyn-generated synthesis routes. These routes, rationalized by density functional theory binding energies, resulted in the successful synthesis of a UFI material with a high Si/Al$_{\text{ICP}}$ of 19.0, which is expected to improve thermal stability and is higher than that of any previously recorded. |
| title | DiffSyn: A Generative Diffusion Approach to Materials Synthesis Planning |
| topic | Materials Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.17094 |