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HlightReaxMD: A Machine Learning-Augmented Multiscale Analysis Framework for Radiation Chemistry Dynamics and Damage

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HlightReaxMD is a new toolkit for analyzing molecular dynamics (MD) simulations of irradiation damage. It extracts chemical reaction and collision cascade data, enabling accurate prediction of material irradiation effects.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Nuclear Engineering

Background:

  • Molecular dynamics (MD) simulations are crucial for studying irradiation-induced material damage.
  • Analyzing complex chemical reactions within MD trajectories presents significant challenges.
  • Existing models like the Norgett-Robinson-Torrens (NRT) displacements per atom (dpa) model have limitations in predicting irradiation damage.

Purpose of the Study:

  • To introduce HlightReaxMD, a novel cross-platform toolkit for analyzing MD simulations of irradiation damage.
  • To enable direct extraction of chemical reaction and collision cascade information from MD trajectories.
  • To develop a machine learning-driven model for predicting irradiation damage beyond traditional methods.

Main Methods:

  • Developed HlightReaxMD, a toolkit supporting all elements in reactive force fields (ReaxFF).
  • Implemented automated analysis of atomic-scale collision events using cascade trees and reaction network paths.
  • Integrated a machine learning model for enhanced irradiation damage prediction.

Main Results:

  • HlightReaxMD provides tools for chemical reaction analysis, kinetic parameter calculation, and collision cascade analysis.
  • The toolkit automates the tracking of atomic events and analysis of reaction mechanisms.
  • The machine learning model offers improved irradiation damage prediction by considering multiple factors.

Conclusions:

  • HlightReaxMD offers a comprehensive solution for analyzing terabyte-level MD trajectory data.
  • The toolkit facilitates systematic research into irradiation effects at atomic to microscale.
  • HlightReaxMD advances the prediction of material irradiation damage, moving beyond the NRT-dpa model.