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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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  1. Home
  3. Data-driven Kinetic Reaction Networks For Separation Chemistry.
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  3. Data-driven Kinetic Reaction Networks For Separation Chemistry.

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Data-Driven Kinetic Reaction Networks for Separation Chemistry.

Jiyoung Lee1,2, Logan J Augustine1, Graeme Henkelman2

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

Journal of Chemical Theory and Computation
|May 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence models enhance understanding of uranium liquid-liquid extraction. Chemistry-informed models offer better interpretability and accuracy for optimizing rare-earth and actinide separations.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Data Science

Background:

  • Understanding complex chemical reactions is crucial for process design.
  • Separating rare-earth and actinide elements requires improved chemical insights.
  • Liquid-liquid extraction is a key process for element separation.

Purpose of the Study:

  • To develop kinetic reaction networks for uranium extraction using AI and machine learning.
  • To compare purely data-driven models with chemistry-informed models.
  • To enhance the interpretability and accuracy of chemical process modeling.

Main Methods:

  • Leveraging artificial intelligence and machine learning for kinetic reaction networks.
  • Developing purely data-driven models with L1 regression.
  • Creating chemistry-informed models using quantum mechanical calculations for reaction energies.
  • Comparing model performance based on experimental data.

    • Main Results:

    • Purely data-driven models are accurate but lack interpretability.
    • Chemistry-informed models show improved interpretability and consistency.
    • Ensemble averaging in chemistry-informed models enhances accuracy.
    • The dominant extracted species is UO2(NO3)2(DEHiBA)2, consistent with experimental data.

    Conclusions:

    • AI and machine learning can effectively model complex chemical reactions.
    • Chemistry-informed models provide valuable insights into separation mechanisms.
    • This approach offers accurate predictions and chemical understanding at low computational cost.
    • The study advances the design and optimization of chemical separation processes.