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Quantum annealing for microstructure equilibration with long-range elastic interactions.

Roland Sandt1, Yann Le Bouar2, Robert Spatschek3,4

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Quantum annealing accelerates simulations for materials like shape memory alloys by efficiently determining microstructures. This method models elastic interactions between grains, outperforming classical algorithms for complex material simulations.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Quantum Computing

Background:

  • Simulating microstructures in materials like shape memory alloys is computationally intensive.
  • Understanding the long-range elastic interactions between grains and martensite variants is crucial for material properties.

Purpose of the Study:

  • To demonstrate the use and benefits of quantum annealing for determining equilibrated microstructures.
  • To compare the performance of quantum annealing with classical algorithms for materials simulations.

Main Methods:

  • Formulating the system's energy as an Ising Hamiltonian.
  • Utilizing distance-dependent elastic interactions between grains to predict variant selection.
  • Applying quantum annealing to simulate microstructures with up to several thousand grains.

Main Results:

  • Quantum annealing significantly accelerates simulations compared to classical algorithms.
  • The approach accurately predicts variant selection based on transformation eigenstrains.
  • Direct representation of arbitrary microstructures is possible, enabling faster simulations.

Conclusions:

  • Quantum annealing offers a powerful and efficient method for simulating complex material microstructures.
  • This approach has the potential to advance the design and understanding of shape memory alloys and other advanced materials.