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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Published on: March 3, 2023

Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations.

Syed Islamuddin Shah1, Giridhar Nandipati, Abdelkader Kara

  • 1Department of Physics, University of Central Florida, Orlando, FL 32816, USA. islamuddin@knights.ucf.edu

Journal of Physics. Condensed Matter : an Institute of Physics Journal
|August 18, 2012
PubMed
Summary
This summary is machine-generated.

We developed a new pattern recognition method for self-learning kinetic Monte Carlo simulations. This approach accurately models atomic island diffusion on surfaces, considering all possible atomic movements.

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Area of Science:

  • Surface science
  • Computational materials science
  • Statistical mechanics

Background:

  • Kinetic Monte Carlo (KMC) simulations are crucial for understanding surface processes.
  • Accurate modeling of atomic diffusion requires considering various adsorption sites and atomic movements.
  • Existing methods may not fully capture the complexity of island dynamics on fcc(111) surfaces.

Purpose of the Study:

  • To develop an advanced pattern recognition scheme for self-learning KMC (SLKMC-II) simulations.
  • To incorporate both face-centered cubic (fcc) and hexagonal close-packed (hcp) adsorption sites into the simulation.
  • To enable the automatic identification and energetic calculation of diverse atomic rearrangement processes.

Main Methods:

  • Developed a novel pattern recognition scheme identifying local atomic environments using hexagonal rings.
  • Implemented the scheme within a self-learning kinetic Monte Carlo (SLKMC-II) framework.
  • Applied the method to simulate the self-diffusion of 9-atom islands (M(9)) on M(111) surfaces (M = Cu, Ag, Ni).

Main Results:

  • The new scheme successfully identifies and analyzes various atomic movements, including shearing, reptation, and concerted gliding.
  • It accounts for fcc-fcc, hcp-hcp, and fcc-hcp atomic site transitions during simulations.
  • Energetics of these processes are calculated dynamically ('on the fly') during the simulation.

Conclusions:

  • The developed pattern recognition scheme enhances the accuracy and scope of SLKMC simulations for surface diffusion.
  • It provides a more comprehensive understanding of atomic island dynamics on fcc(111) surfaces.
  • This method is applicable to studying self-diffusion of metal islands (Cu, Ag, Ni).