Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics

Soumya Mondal; Subhanu Halder; Debarchan Basu; Sandeep Kumar; Tarak Karmakar

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arXiv · 2507.16531

Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics

Soumya Mondal·Subhanu Halder·Debarchan Basu·Sandeep Kumar·Tarak Karmakar

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Abstract

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.

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Soumya Mondal, Subhanu Halder, Debarchan Basu, Sandeep Kumar, Tarak Karmakar. 2025-07-22. Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics. https://arxiv.org/abs/2507.16531

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