High-Tc superconductor candidates proposed by machine learning

TL;DR

This paper uses machine learning models to predict and identify high-temperature superconductor candidates from a large materials database, achieving accurate Tc predictions and ranking potential materials.

Contribution

It introduces a statistical learning approach with ridge regression models to predict Tc and efficiently screen materials for high-temperature superconductivity.

Findings

Achieved ~5 K average prediction error for unseen materials.

Predicted top candidates with Tc around 315-316 K.

Successfully ranked materials considering stability and band gap.

Abstract

We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge regression models on experimental SuperCon data achieve average Tc prediction errors of ~5 K for unseen out-of-sample materials. Two models were trained with one excluding high pressure data in training ("ambient" model) and a second also including high pressure data ("implicit" model). Subsequent utilization of the approach to scan ~153k materials in the Materials Project enables the ranking of candidates by Tc while accounting for thermodynamic stability and small band gap. The ambient model is used to predict stable top three high-Tc candidate materials that include those with large band gaps of LiCuF4 (316 K), Ag2H12S(NO)4 (316 K), and Na2H6PtO6 (315 K). Filtering these candidates for those with small band gaps correspondingly yields LiCuF4 (316 K), Cu2P2O7 (311 K), and Cu3P2H2O9 (307 K).