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Machine learning potentials accelerate molecular modeling for alloy-based solid propellants. Lithium doping enhances combustion reactivity and speeds up the process by 10% in AlLi-AP interface reactions.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Engineering

Background:

  • Molecular modeling faces accuracy-efficiency challenges, especially for interface reactions.
  • Exploring chemical spaces requires high accuracy and efficiency.
  • Alloy-based solid propellants offer promising performance characteristics.

Purpose of the Study:

  • To develop an efficient and accurate machine learning workflow for modeling interface reactions in alloy-based solid propellants.
  • To investigate the combustion behavior of AlLi-AP interface reactions.
  • To understand the impact of lithium doping on propellant combustion.

Main Methods:

  • Utilized the SOAP descriptor and Principal Component Analysis (PCA) to capture potential energy surface features.
  • Constructed a neural network potential model for AlLi-AP interface reactions.
  • Performed large-scale molecular dynamics (MD) simulations.

Main Results:

  • The neural network potential model demonstrated excellent predictive accuracy for energy, forces, and bond energies.
  • Lithium doping significantly enhanced reactivity and reduced thermal conductivity during the initial combustion stage.
  • Lithium exhibited a diffusion coefficient three times greater than aluminum, accelerating overall combustion by approximately 10%.

Conclusions:

  • The developed machine learning workflow effectively models complex interface reactions.
  • Lithium doping is a key factor in optimizing the combustion performance of AlLi-AP propellants.
  • This approach enables virtual screening and rational design of advanced propellant formulations.