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LeaPP: Learning Pathways to Polymorphs through Machine Learning Analysis of Atomic Trajectories.

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LeaPP categorizes crystal nucleation trajectories using particle history, offering a dynamic, unsupervised approach. This method reveals distinct pathways and predicts resulting polymorphs, advancing self-assembly understanding.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Physics

Background:

  • Crystal nucleation and growth are vital for technological applications.
  • Molecular simulations are key for studying short-timescale nucleation.
  • Current methods analyzing static snapshots may miss crucial dynamic information.

Purpose of the Study:

  • Introduce LeaPP, a novel methodology for categorizing nucleation trajectories.
  • Incorporate temporal information of constituent particles for enhanced analysis.
  • Provide a more nuanced understanding of crystal nucleation mechanisms.

Main Methods:

  • Categorize nucleation trajectories based on temporal particle data.
  • Analyze the time evolution of local particle environments.
  • Utilize an unsupervised approach without traditional order parameters.

Main Results:

  • Distinguish between different evolving particle paths based on dynamics.
  • Characterize nucleation trajectories into distinct pathways.
  • Demonstrate LeaPP's predictive power for resulting polymorphs across multiple systems.

Conclusions:

  • LeaPP offers a nuanced, dynamic understanding of crystal nucleation.
  • The methodology is applicable to diverse self-assembly problems.
  • Temporal analysis of particle evolution is crucial for understanding complex processes.