MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911145253666816 |
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| author | Ferguson, Kevin Chen, Yu-hsuan Kara, Levent Burak |
| author_facet | Ferguson, Kevin Chen, Yu-hsuan Kara, Levent Burak |
| contents | The discovery and study of new material systems rely on molecular simulations that often come with significant computational expense. We propose MDDM, a Molecular Dynamics Diffusion Model, which is capable of predicting a valid output conformation for a given input pair potential function. After training MDDM on a large dataset of molecular dynamics self-assembly results, the proposed model can convert uniform noise into a meaningful output particle structure corresponding to an arbitrary input potential. The model's architecture has domain-specific properties built-in, such as satisfying periodic boundaries and being invariant to translation. The model significantly outperforms the baseline point-cloud diffusion model for both unconditional and conditional generation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly Ferguson, Kevin Chen, Yu-hsuan Kara, Levent Burak Machine Learning Computational Physics The discovery and study of new material systems rely on molecular simulations that often come with significant computational expense. We propose MDDM, a Molecular Dynamics Diffusion Model, which is capable of predicting a valid output conformation for a given input pair potential function. After training MDDM on a large dataset of molecular dynamics self-assembly results, the proposed model can convert uniform noise into a meaningful output particle structure corresponding to an arbitrary input potential. The model's architecture has domain-specific properties built-in, such as satisfying periodic boundaries and being invariant to translation. The model significantly outperforms the baseline point-cloud diffusion model for both unconditional and conditional generation tasks. |
| title | MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2501.17319 |