Prediction of High Transition Temperatures in Ceramic Superconductors

TL;DR

This paper introduces a Bayesian probability-based model that successfully predicts high transition temperatures in ceramic superconductors, including non-cuprate materials, by focusing on a self-organized dopant network.

Contribution

It proposes a new homology class model based on a glassy, self-organized dopant network that outperforms previous theories in predicting superconducting transition temperatures.

Findings

Model accurately predicts transition temperatures for various ceramics.

Success extends to non-cuprate superconductors like FeAs-based materials.

Highlights limitations of first-principle and CuO2-based models.

Abstract

The prediction of transition temperatures can be regarded in several ways, either as an exacting test of theory, or as a tool for identifying theoretical rules for defining new homology models. Popular "first principle" methods for predicting transition temperatures in conventional crystalline superconductors have failed for cuprate HTSC, as have parameterized models based on CuO2 planes (with or without apical oxygen). Following a path suggested by Bayesian probability, we find that the glassy, self-organized dopant network percolative model is so successful that it defines a new homology class appropriate to ceramic superconductors. The reasons for this success are discussed, and a critical comparison is made with previous theories. The predictions are successful for all ceramics, including new non-cuprates based on FeAs in place of CuO2.