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Machine Learning Nucleation Collective Variables with Graph Neural Networks.

Florian M Dietrich1, Xavier R Advincula1, Gianpaolo Gobbo2

  • 1Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, U.K.

Journal of Chemical Theory and Computation
|October 25, 2023
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Summary
This summary is machine-generated.

We developed a graph-based model to efficiently calculate nucleation collective variables (CVs), significantly speeding up simulations for nucleation processes. This computational advance aids in studying complex material formation.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Calculating nucleation collective variables (CVs) is computationally intensive, limiting enhanced sampling methods for nucleation studies.
  • Efficient CV calculation is crucial for investigating nucleation in realistic systems.

Purpose of the Study:

  • To develop a computationally efficient graph-based model for approximating nucleation CVs.
  • To enhance the speed of on-the-fly evaluation of nucleation CVs for enhanced sampling methods.

Main Methods:

  • Developed a graph-based model for nucleation CV approximation.
  • Performed simulations on a nucleating colloidal system.
  • Assessed model efficiency using pulling, umbrella sampling, and metadynamics.
  • Tested transferability of graph-based CVs to a different system (copper melt nucleation).

Main Results:

  • Achieved orders-of-magnitude gains in computational efficiency for on-the-fly CV evaluation.
  • Demonstrated the model's effectiveness in postprocessing and biasing nucleation trajectories.
  • Showcased transferability of graph-based CVs across different systems, including colloidal and crystalline copper nucleation.

Conclusions:

  • The graph-based model significantly improves computational efficiency for nucleation CV calculations.
  • The approach is general and shows promise for application to more complex systems and diverse CVs.
  • This method facilitates the investigation of nucleation processes using enhanced sampling techniques.