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Descriptor-Free Collective Variables from Geometric Graph Neural Networks.

Jintu Zhang1,2, Luigi Bonati2, Enrico Trizio2

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This study introduces a fully automatic method for designing collective variables (CVs) using graph neural networks, bypassing the need for user-defined descriptors. This approach enhances the computational study of rare events by directly learning relevant physics from atomic coordinates.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics
  • Machine Learning

Background:

  • Enhanced sampling simulations are crucial for studying rare events in molecular systems.
  • Collective variables (CVs) are essential for reducing dimensionality in these simulations.
  • Current methods often require user-defined physical descriptors and lack full automation.

Purpose of the Study:

  • To develop a fully automatic approach for determining collective variables (CVs).
  • To bypass the need for user-defined physical descriptors in CV design.
  • To ensure CVs are invariant under relevant physical symmetries, particularly permutation.

Main Methods:

  • Utilized graph neural networks (GNNs) to directly process atomic coordinates as input.
  • Developed a machine learning approach for automated CV determination.
  • Integrated analysis tools to aid in the physical interpretation of learned CVs.

Main Results:

  • Achieved a fully automatic CV determination method using GNNs.
  • Demonstrated that the learned CVs are invariant under relevant symmetries, including permutation.
  • Validated the robustness and efficacy of the approach across various systems (peptide, ion dissociation, chemical reaction).

Conclusions:

  • The proposed GNN-based method offers a powerful and automated way to define CVs for enhanced sampling simulations.
  • This approach simplifies the study of rare events by removing the need for manual feature engineering.
  • The method's applicability to diverse systems highlights its general utility in computational chemistry and physics.