KNU Department of Physics Education Team led by Sooran Kim, Discovers Machine-Learning-Guided Prediction Models of Critical Temperature of Cuprates
KNU Department of Physics Education Team led by Professor Sooran Kim collaborated in joint research with Harvard University School of Engineering and Applied Sciences Team to develop a model for predicting the critical temperature of cuprate superconductors via machine-learning and Ab initio quantum chemistry methods, and to guide the experimental search for new cuprate superconductors. The paper on these results was published on the front page of the highly renowned journal in physics, “The Journal of Physical Chemistry Letters” on July 8. The first author for the paper was Dongeon Lee, KNU Department of Physics Education.
Cuprate superconductors are known to be substances with the highest experimental maximum superconducting transition temperature (Tc,max), although their underlying mechanism of superconductivity remains elusive. Thus, in this study, KNU Department of Physics Education Team develops ML models for predicting Tc,max of hole-doped cuprates by using material-dependent parameters beyond simple composition features. Additionally in the search for new cuprate candidates, the team has found that cuprates with Ga as an apical cation exhibited Tc,max comparable to that of cuprates with Hg as an apical cation, suggesting that machine learning could guide the design of high-Tc superconductors in the future.
Professor Sooran Kim states, “This research shows significant evidence of high accuracy for prediction models of the critical temperature of cuprates via machine learning and Ab initio quantum chemistry methods. Moreover, it assists in developing a quantitative understanding of the underlying mechanism of cuprates as well as in guiding the search for new cuprate superconductors. In fact, we are undertaking a follow-up study with machine learning on a different superconductor at present.”