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    KNU professor predicts the critical temperature of Cuprate Superconductors using machine learning

    Soo Ran Kim, a professor of Kyungpook National University in the Physics Education Department and Harvard University’s research team developed a critical temperature prediction equation for copper-based superconductors using machine learning and first principle calculation and proposed a new copper superconductor.

    The results of the study were published in a July 8 cover paper of The Journal of Physical Chemistry Letters, an international journal of prestigious physics and chemistry. The first author is Dong Geon Lee, an undergraduate in physics education.

    Cuprate superconductors are materials with the highest superconductivity critical temperature (the temperature at which resistance becomes ‘0’) at atmospheric pressure, and a clear mechanism of superconductivity has yet to be identified.

    Professor Soo Ran Kim’s team developed a formula for cuprate superconductors using data-based machine learning technology without existing mechanisms. A new cuprate superconductor using Ga was proposed as a model developed in conjunction with this. A critical temperature similar to that of cuprate superconductors with the highest critical temperature was predicted for the proposed superconductor.

    Professor Soo Ran Kim said, “This study is significant in that it developed a formulation of critical temperature that had never existed with high prediction using machine learning and first-principle calculation. It is also thought that it will help to understand the mechanisms of cuprate superconductors quantitatively and guide the experimental discovery of new superconductors. “We are currently working on another superconductor with machine learning.”

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