Jun 24, 2019 | Updated: 08:43 AM EDT

Improving Superconductors Through Artificial Intelligence and Principles of Artifical Selection

May 27, 2019 09:01 PM EDT

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Artificial Intelligence
(Photo : Pixabay)
Superconductor Evolution (IMAGE)
(Photo : Argonne National Laboratory/Andreas Glatz) This image depicts the algorithmic evolution of a defect structure in a superconducting material. Each iteration serves as the basis for a new defect structure. Redder colors indicate a higher current-carrying capacity.

Researchers from the U.S. Department of Energy's (DOE) Argonne National Laboratory utilized sets of defects to improve on the capacity of superconductors. Artificial intelligence and high-performance supercomputers were utilized to treat each defect like a biological gene. Findings were published in the Proceedings of the National Academy of Sciences

The principle of using defects in developing a superconductor is based on targeted evolution. Prizewinning horses are carefully bred by owners of thoroughbred stallions in order to win in races. Scientists use artificial selection in developing superconductors that efficiently transmit electric current.

Contrary to public knowledge, most applied superconductors work at high magnetic fields because they have defects. The overall combination of defects within a superconductor contribute to its capacity to conduct electric current. However, it would not be great to have numerous defects as this can block the electric current pathway.

Each defect was treated like a biological gene. Different amounts of current by superconductors were a result of the different combinations of defects. The researchers developed a computer algorithm that worked on sets of defects. If they find out that there are advantages with a specific set of defects, the computer would re-initialize and new combinations of defects will come out.

"Each run of the simulation is equivalent to the formation of a new generation of defects that the algorithm seeks to optimize," said Argonne distinguished fellow and senior materials scientist Wai-Kwong Kwok, an author of the study. "Over time, the defect structures become progressively refined, as we intentionally select for defect structures that will allow for materials with the highest critical current."

Superconductors "rely" on defects as these trap and anchor magnetic vortices that form in the presence of a magnetic field. Application of a current allow the vortices to freely move within a pure superconducting material. When this occurs, these vortices produce a resistance that counterattacks a superonducting effect. Scientists always try to find methods on how to transmit electricity without loss.

To find the right combination of defects to arrest the motion of the vortices, the researchers initialized their algorithm with defects of random shape and size. While the researchers knew this would be far from the optimal setup, it gave the model a set of neutral initial conditions from which to work. As the researchers ran through successive generations of the model, they saw the initial defects transform into a columnar shape and ultimately a periodic arrangement of planar defects.

"When people think of targeted evolution, they might think of people who breed dogs or horses," said Argonne materials scientist Andreas Glatz, the corresponding author of the study. "Ours is an example of materials by design, where the computer learns from prior generations the best possible arrangement of defects."

Certain issues regarding the process of artificial defect selection involve specific defect patterns to be engrossed in the model which could lead to calcification of the genetic data.

"In a certain sense, you can kind of think of it like inbreeding," Kwok said. "Conserving most information in our defect 'gene pool' between generations has both benefits and limitations as it does not allow for drastic systemwide transformations. However, our digital 'evolution' can be repeated with different initial seeds to avoid these problems."

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