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<p>We introduce a coarse-grained all-atom force field (CGAA-FF) that integrates coarse-grained message passing into all-atom force fields using equivariant graph models. By embedding atomistic coordinates into grain-level nodes, CGAA-FF predicts grain-level energies and atom-level forces. Tested on EC/EMC electrolytes and crystalline and disordered RDX phases, it achieves 0.201 and 0.253 eV Å<sup>−1</sup> force errors, with ~10-fold speed and ~5-fold memory improvements over conventional MLIPs, enabling efficient soft-matter simulations.</p>

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Coarse-grained graph architectures for all-atom force predictions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Sungwoo Kang,
  • Jinwoong Chae

摘要

We introduce a coarse-grained all-atom force field (CGAA-FF) that integrates coarse-grained message passing into all-atom force fields using equivariant graph models. By embedding atomistic coordinates into grain-level nodes, CGAA-FF predicts grain-level energies and atom-level forces. Tested on EC/EMC electrolytes and crystalline and disordered RDX phases, it achieves 0.201 and 0.253 eV Å−1 force errors, with ~10-fold speed and ~5-fold memory improvements over conventional MLIPs, enabling efficient soft-matter simulations.