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

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.

References in corpus (14)

• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Evidence for ubiquitous strong electron-phonon coupling in high-temperature superconductors
• Direct observation of competition between superconductivity and charge density wave order in YBa2Cu3Oy
• Big Data of Materials Science - Critical Role of the Descriptor
• Band-structure trend in hole-doped cuprates and correlation with Tcmax
• Quantum Magnetic Excitations from Stripes in Copper-Oxide Superconductors
• Machine learning modeling of superconducting critical temperature
• An unusual isotope effect in a high-transition-temperature superconductor
• Scaling of the transition temperature of hole-doped cuprate superconductors with the charge-transfer energy
• Interplay of Electron-Phonon Interaction and Electron Correlation in High Temperature Superconductivity
• Predicting new superconductors and their critical temperatures using unsupervised machine learning
• Functional Form of the Superconducting Critical Temperature from Machine Learning
• Fermi pockets and correlation effects in underdoped YBa2Cu3O6.5
• A picture of pseudogap phase related to charge fluxes

Cited by in corpus (4)

• Machine Learning Prediction Models for Solid Electrolytes based on Lattice Dynamics Properties
• Superconductivity in bilayer $t$-$t^{\prime }$ Hubbard models
• Classification of magnetic order from electronic structure by using machine learning
• Simple predictors of $T_c$ in superconducting cuprates and the role of interactions between effective Wannier orbitals in the $d-p$ 3-band model