Machine-Learning-Guided Prediction Models of Critical Temperature of Cuprates

TL;DR

This study uses machine learning and first-principles calculations to accurately predict the maximum critical temperature of hole-doped cuprates, identifying key material parameters and guiding the design of new high-temperature superconductors.

Contribution

It introduces a predictive model for Tc,max of cuprates with high accuracy and explores hypothetical structures to suggest avenues for designing higher-Tc superconductors.

Findings

Achieved a root-mean-square-error of 3.705 K in Tc,max prediction.

Identified key features influencing Tc,max, such as Bader charge and bond strength.

Predicted higher Tc,max for hypothetical cuprates with specific apical cations.

Abstract

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.