Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909699124756480 |
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| author | Mondal, Soumya Halder, Subhanu Basu, Debarchan Kumar, Sandeep Karmakar, Tarak |
| author_facet | Mondal, Soumya Halder, Subhanu Basu, Debarchan Kumar, Sandeep Karmakar, Tarak |
| contents | Coarse-grained (CG) molecular dynamics (MD) simulations can simulate large molecular complexes over extended timescales by reducing degrees of freedom. A critical step in CG modeling is the selection of the CG mapping algorithm, which directly influences both accuracy and interpretability of the model. Despite progress, the optimal strategy for coarse-graining remains a challenging task, highlighting the necessity for a comprehensive theoretical framework. In this work, we present a graph-based coarsening approach to develop CG models. Coarse-grained sites are obtained through edge contractions, where nodes are merged based on a local variational cost metric while preserving key spectral properties of the original graph. Furthermore, we illustrate how Message Passing Atomic Cluster Expansion (MACE) can be applied to generate ML-CG potentials that are not only highly efficient but also accurate. Our approach provides a bottom-up, theoretically grounded computational method for the development of systematically improvable CG potentials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics Mondal, Soumya Halder, Subhanu Basu, Debarchan Kumar, Sandeep Karmakar, Tarak Soft Condensed Matter Coarse-grained (CG) molecular dynamics (MD) simulations can simulate large molecular complexes over extended timescales by reducing degrees of freedom. A critical step in CG modeling is the selection of the CG mapping algorithm, which directly influences both accuracy and interpretability of the model. Despite progress, the optimal strategy for coarse-graining remains a challenging task, highlighting the necessity for a comprehensive theoretical framework. In this work, we present a graph-based coarsening approach to develop CG models. Coarse-grained sites are obtained through edge contractions, where nodes are merged based on a local variational cost metric while preserving key spectral properties of the original graph. Furthermore, we illustrate how Message Passing Atomic Cluster Expansion (MACE) can be applied to generate ML-CG potentials that are not only highly efficient but also accurate. Our approach provides a bottom-up, theoretically grounded computational method for the development of systematically improvable CG potentials. |
| title | Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2507.16531 |