  Cuprates, a member of high-Tc superconductors, have been on the long-debate
on their superconducting mechanism, so that predicting the critical temperature
of cuprates still remains elusive. Herein, using machine learning and first
principle calculations, we predict the maximum superconducting transition
temperature (Tc,max) of hole-doped cuprates and suggest the explicit functional
form for Tc,max with the root-mean-square-error of 3.705 K and the coefficient
of determination R2 of 0.969. We employed two machine learning models; one is a
parametric brute force searching method and another is a non-parametric random
forest regression model. We have found that material dependent parameters such
as the Bader charge of apical oxygen, the bond strength between apical atoms,
and the number of superconducting layers are important features to estimate
Tc,max. Furthermore, we predict the Tc,max of hypothetical cuprates generated
by replacing apical cations with other elements. When Ga is an apical cation,
the predicted Tc,max is the highest among the hypothetical structures with 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.
