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Machine learning dynamic correlation in chemical kinetics.

Changhae Andrew Kim1, Nathan D Ricke1, Troy Van Voorhis1

  • 1Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

The Journal of Chemical Physics
|October 16, 2021
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Summary
This summary is machine-generated.

Machine learning (ML) creates accurate moment closures for chemical kinetics, improving simulations of surface reactions. ML moment closure (MLMC) offers a computationally efficient alternative to traditional methods like pair approximation (PA).

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Area of Science:

  • Computational chemistry
  • Surface science
  • Chemical kinetics

Background:

  • Lattice models are crucial for simulating surface reaction kinetics.
  • Propagating entire lattice configurations is computationally expensive.
  • Moment closure approximations simplify these simulations but struggle with long-range correlations.

Purpose of the Study:

  • To investigate the use of machine learning (ML) for developing accurate moment closures in chemical kinetics.
  • To address the limitations of traditional closures like mean-field and pair approximation (PA).
  • To utilize the lattice Lotka-Volterra model as a test system.

Main Methods:

  • Training feedforward neural networks on kinetic Monte Carlo (KMC) simulation data.
  • Using KMC results for specific rate constants and initial conditions.
  • Developing a machine learning moment closure (MLMC) approach.

Main Results:

  • MLMC accurately predicts three-site occupation probabilities with the same input as PA.
  • MLMC significantly improves the simulation of dynamics and dynamical regimes.
  • MLMC demonstrates superior performance compared to traditional closure methods.

Conclusions:

  • MLMC is a powerful tool for enhancing the accuracy of lattice-based kinetic simulations.
  • MLMC offers a computationally efficient method for interpolating KMC data.
  • MLMC enables researchers to gain insights at a reduced computational cost.