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Graph neural network based coarse-grained mapping prediction.

Zhiheng Li1, Geemi P Wellawatte2, Maghesree Chakraborty3

  • 1Department of Computer Science, University of Rochester USA.

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This study introduces the Deep Supervised Graph Partitioning Model (DSGPM) for automated coarse-grained (CG) mapping operator selection in molecular dynamics (MD) simulations. DSGPM accurately predicts optimal operators, improving CG model quality.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Biophysics

Background:

  • Coarse-grained (CG) molecular dynamics (MD) simulations require accurate mapping operators for effective coarse-graining.
  • Manual selection of these operators by experts is the current standard but is time-consuming and lacks theoretical rigor.
  • Developing automated methods for CG mapping operator selection is crucial for advancing CG MD simulations.

Purpose of the Study:

  • To develop and validate an automated approach for selecting coarse-grained (CG) mapping operators.
  • To present a novel graph neural network (GNN) based model, the Deep Supervised Graph Partitioning Model (DSGPM), for predicting CG mapping operators.
  • To establish a new benchmark dataset, Human-annotated Mappings (HAM), for training and evaluating CG mapping prediction models.

Main Methods:

  • Developed Deep Supervised Graph Partitioning Model (DSGPM), a graph neural network (GNN) model, to treat CG mapping operator selection as a graph segmentation problem.
  • Created and utilized the Human-annotated Mappings (HAM) dataset, comprising 1180 molecules with expert-annotated mapping operators, for supervised learning.
  • Employed a novel metric learning objective to generate high-quality atomic features for spectral clustering.

Main Results:

  • The DSGPM model demonstrated superior performance compared to existing state-of-the-art methods in graph segmentation tasks.
  • The HAM dataset provides a valuable resource for future research in automated CG mapping.
  • CG MD models generated using DSGPM-predicted mapping operators exhibited good simulation performance.

Conclusions:

  • The DSGPM offers an effective and automated solution for CG mapping operator selection, addressing a critical need in CG MD simulations.
  • The developed model and dataset advance the field of computational chemistry and materials science by enabling more efficient and accurate molecular modeling.
  • Automated prediction of CG mapping operators using DSGPM leads to reliable CG MD models suitable for simulation.