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This study introduces an adaptive cooling method for simulated annealing, improving computational efficiency for atomistic structure optimization. The new approach enhances speed by approximately twofold for Lennard-Jones clusters.

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Area of Science:

  • Computational physics
  • Materials science
  • Chemical physics

Background:

  • Simulated annealing is a robust global optimization technique.
  • Existing variations focus on improving the cooling schedule.
  • Optimizing atomistic structures requires efficient global optimization methods.

Purpose of the Study:

  • To introduce a novel molecular dynamics-based simulated annealing variant.
  • To utilize system heat capacity for adaptive cooling rate control.
  • To enhance computational efficiency in atomistic structure optimization.

Main Methods:

  • Developed a molecular dynamics-based simulated annealing algorithm.
  • Integrated on-the-fly heat capacity determination.
  • Applied the adaptive cooling method to Lennard-Jones clusters.

Main Results:

  • The adaptive cooling approach demonstrated increased computational efficiency.
  • Efficiency improvements were approximately twofold for 25-40 atom clusters.
  • Efficiency gains increased with system size.

Conclusions:

  • Adaptive cooling based on heat capacity offers a more efficient alternative to classical simulated annealing.
  • This method is particularly effective for optimizing atomistic structures.
  • The approach shows promise for larger and more complex systems.