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Efficient hybrid evolutionary optimization of interatomic potential models.

W Michael Brown1, Aidan P Thompson, Peter A Schultz

  • 1Discrete Mathematics and Complex Systems, Sandia National Laboratories, Albuquerque, New Mexico 87185-1316, USA. wmbrown@sandia.gov

The Journal of Chemical Physics
|January 26, 2010
PubMed
Summary
This summary is machine-generated.

Developing accurate atomistic models for materials is challenging. This study introduces a hybrid genetic programming strategy that automates the creation of interatomic potentials, improving simulation accuracy and efficiency.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Physics

Background:

  • Accurate atomistic empirical models are crucial for materials simulations, but their development is often hindered by a lack of predictive power.
  • Existing methods struggle with generating reliable interatomic potentials for diverse material systems.

Purpose of the Study:

  • To advance the automated development of material-specific interatomic potentials using a hybrid, population-based optimization strategy.
  • To compare different genetic programming strategies for their effectiveness in generating transferable potentials.

Main Methods:

  • Implemented a hybrid, population-based optimization strategy for automated interatomic potential development.
  • Compared two parallel genetic programming strategies: standard and Hierarchical Fair Competition.
  • Evaluated hybrid local search, system energies, particle forces, and a correlation-based fitness statistic.
  • Investigated the impact of system size and employed vectorization to optimize computation.

Main Results:

  • The Hierarchical Fair Competition algorithm demonstrated superior transferability compared to other strategies, despite slightly lower training set accuracy.
  • A correlation-based fitness statistic significantly reduced computation time.
  • Problem difficulty scales with the number of atoms, but vectorization mitigates this.
  • The method successfully recreated known two- and three-body interatomic potentials using only system energies and particle forces.

Conclusions:

  • The developed hybrid genetic programming strategy offers an efficient and effective approach for automated interatomic potential development.
  • This method enhances the transferability and predictive accuracy of models for materials simulations.
  • The approach holds promise for accelerating materials discovery and design through reliable atomistic simulations.