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Our analytically based technique for coarse-graining (CG) polymer simulations dramatically improves spatial and temporal scaling while preserving thermodynamic quantities and bulk properties. The purpose of CG codes is to run more efficient molecular dynamics simulations, yet the research field generally lacks thorough analysis of how such codes scale with respect to full-atom representations. This paper conducts an in-depth performance study of highly realistic polymer melts on modern supercomputing systems. We also present a workflow that integrates our analytical solution for calculating CG forces with new high-performance techniques for mapping back and forth between the atomistic and CG descriptions in LAMMPS. The workflow benefits from the performance of CG, while maintaining full-atom accuracy. Our results show speedups up to 12x faster than atomistic simulations.
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Is preservation of symmetry necessary for coarse-graining?
There is a need for theory on how to group atoms in a molecule to define a coarse-grained (CG) mapping. This article investigates the importance of preserving symmetry of the underlying molecular graph of a given molecule when choosing a CG mapping. 26 CG models of seven alkanes with three different CG techniques were examined. We unexpectedly find preserving symmetry has no consistent effect on CG model accuracy regardless of CG method or comparison metric.
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- Award ID(s):
- 1764415
- PAR ID:
- 10166428
- Date Published:
- Journal Name:
- Physical Chemistry Chemical Physics
- ISSN:
- 1463-9076
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1180 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally, we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d0cp02309d
