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Using machine learning with atomistic surface and local water features to predict heterogeneous ice nucleation.

Abhishek Soni1, G N Patey1

  • 1Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada.

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Machine learning and molecular dynamics simulations predict heterogeneous ice nucleation (HIN) likelihood. Local water features near surfaces are key predictors, guiding the design of ice nucleating particles (INPs).

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Area of Science:

  • Physical Chemistry
  • Materials Science
  • Climate Science

Background:

  • Heterogeneous ice nucleation (HIN) is crucial for climate, nanotechnology, and cryopreservation.
  • Identifying effective ice nucleating particles (INPs) is challenging due to limitations in experimental microscopic resolution.
  • Understanding the molecular-level determinants of INP efficacy is a fundamental scientific question.

Purpose of the Study:

  • To develop a predictive model for heterogeneous ice nucleation (HIN) using molecular dynamics (MD) and machine learning (ML).
  • To identify key molecular features of surfaces and surrounding water that govern ice nucleation propensity.
  • To overcome the computational cost limitations of traditional MD simulations for observing HIN.

Main Methods:

  • Employed MD simulations to generate atomistic data for 153 different surfaces and their interactions with water.
  • Extracted local water features from short MD simulations (≤300 ns) before nucleation initiation.
  • Trained and evaluated three ML classification models (Random Forest, Support Vector Machine, Gaussian Process) to predict HIN likelihood.

Main Results:

  • Machine learning models achieved high accuracy (0.89 ± 0.05 for Random Forest) in predicting HIN likelihood.
  • Local water features, such as icelike structures and density/polarization profiles, were found to be essential for accurate predictions.
  • Including only local water features provided accuracy comparable to models using both surface and water features, indicating local water structure encapsulates surface influence.

Conclusions:

  • This combined MD-ML approach effectively predicts heterogeneous ice nucleation propensity.
  • Local water structure near a surface is a critical determinant of ice nucleation, simplifying the search for effective INPs.
  • The findings provide a framework for designing novel materials that either promote or inhibit ice nucleation for various applications.