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A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo.

Martín Leandro Paleico1, Jörg Behler1

  • 1Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Tammannstraße 6, 37077 Göttingen, Germany.

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Summary
This summary is machine-generated.

A new flexible grid algorithm enhances global optimization in atomistic simulations. This method improves efficiency by using grids without biasing results, crucial for complex systems like material interfaces.

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Area of Science:

  • Computational Physics
  • Materials Science
  • Chemical Physics

Background:

  • Global optimization is critical for atomistic simulations, exploring complex potential energy surfaces.
  • Basin hopping Monte Carlo is a common method, but high-dimensional search spaces are computationally demanding.
  • Existing grid-based methods offer efficiency but risk biasing results with fixed grids.

Purpose of the Study:

  • To introduce a flexible grid algorithm for global optimization in atomistic simulations.
  • To enable efficient exploration of potential energy surfaces without introducing bias.
  • To provide a general method applicable to diverse and heterogeneous systems.

Main Methods:

  • Development of a novel flexible grid algorithm for atomic position representation.
  • Integration of the flexible grid approach into a global optimization framework.
  • Benchmarking the algorithm using Lennard-Jones clusters up to 100 particles.

Main Results:

  • The flexible grid algorithm successfully reduces the search space without biasing simulation outcomes.
  • Demonstrated applicability to heterogeneous systems, including material interfaces and supported clusters.
  • Validated performance on challenging Lennard-Jones cluster global optimization problems.

Conclusions:

  • The flexible grid algorithm offers an efficient and unbiased approach to global optimization in atomistic simulations.
  • This method enhances the study of complex materials and nanoscale systems.
  • It provides a robust tool for tackling challenging global optimization problems in various scientific domains.