Argonne National Laboratory

Research Highlight | Materials Science

Targeted evolution of superconducting materials
In a study published in PNAS, researchers developed a targeted evolution genetic algorithm to create a vortex-pinning genome that can be used to enhance the current carrying capability of high temperature superconductors.
Sketch of a targeted evolution of the pinning landscape.
Sketch of a targeted evolution of the pinning landscape. We start with generation 0, which contains a single configuration without defects. Each defect has elliptical shape and is characterized by three independent diameters. The evolution process mutates the pinning landscape by adding/removing, translating, scaling, and reshaping particles. These mutations create the next generation. We accept the pinning landscape with maximal critical current density (Jc) and discard all others. The evolution ends at some generation N with configuration having maximal Jc (shown in red).

Scientific Achievement

We developed a targeted evolution genetic algorithm to create a vortex-pinning genome that can be used to enhance the current carrying capability of high temperature superconductors.

Significance and Impact

The novel simulation tool advances the materials-by-design paradigm using evolution-based processes and can be adapted to improve the functional properties of advanced materials.

Research Details

  • Use of leadership class computing and genetic algorithm based artificial intelligence
  • Numerical targeted selection used to automatically recognize defect configurations for better vortex pinning
  • Selected configuration is used as seed for a new population generation of defects and the system topology evolves to a defect landscape with the highest critical current

Work was performed at Argonne LCF, Oak Ridge LCF, and Computing Facility at Northern Illinois University.

DOI: 10.1073/pnas.1817417116

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