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A Moving Front Kinetic Monte Carlo Algorithm for Moving Interface Systems.

Donovan ChaffartSonglin ShiChen MaCunjing LvLuis A Ricardez-Sandoval
Published in: The journal of physical chemistry. B (2022)
This work presents the development of a new kinetic Monte Carlo algorithm, referred to as Moving Front kinetic Monte Carlo (MFkMC), for simulating processes subject to moving interfaces. This framework is designed to capture the movement of transiently varying interfaces in a kinetic-like manner so that its movement can be described using Monte Carlo sampling. The MFkMC algorithm accomplishes this task by evaluating the behavior of the interfacial molecules and assigning kinetic Monte Carlo-style rate equations that describe the transition probability that a molecule would advance into the neighboring phase, displacing an interfacial molecule from the opposing phase and thus changing the interface. Due to its kinetic Monte Carlo structure, the MFkMC algorithm can additionally account for other important interfacial phenomena, such as interfacial surface reactions. The proposed algorithm was tested via applications to three different simple interfacial case studies. These studies validate the MFkMC algorithm and demonstrate its capabilities to accurately and efficiently simulate a variety of different moving interface systems.
Keyphrases
  • monte carlo
  • machine learning
  • deep learning
  • ionic liquid
  • molecular dynamics simulations
  • neural network
  • electron transfer