posted on 2021-07-01, 15:04authored byDongeon Lee, Daegun You, Dongwoo Lee, Xin Li, Sooran Kim
Cuprates have been at the center
of long debate regarding their
superconducting mechanism; therefore, predicting the critical temperatures
of cuprates remains elusive. Herein, using machine learning and first-principles
calculations, we predict the maximum superconducting transition temperature
(Tc,max) of hole-doped cuprates and suggest
the functional form for Tc,max with the
root-mean-square-error of 3.705 K and R2 of 0.969. We have found that the Bader charge of apical oxygen,
the bond strength between apical atoms, and the number of superconducting
layers are essential to estimate Tc,max. Furthermore, we predict the Tc,max of
hypothetical cuprates generated by replacing apical cations with other
elements. Among the hypothetical structures, the cuprates with Ga
show the highest predicted Tc,max values,
which are 71, 117, and 131 K for one, two, and three CuO2 layers, respectively. These findings suggest that machine learning
could guide the design of new high-Tc superconductors
in the future.