High- T c superconductor candidates proposed by machine learning

Abstract

Abstract We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature ( T c ) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge regression models on experimental SuperCon data achieves average T c 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 ∼153 k materials in the Materials Project enables the ranking of candidates by T c while accounting for thermodynamic stability and small band gap. The ambient model is used to predict stable top three high- T c candidate materials that include those with large band gaps of LiCuF 4 (316 K), Ag 2 H 12 S(NO) 4 (316 K), and Na 2 H 6 PtO 6 (315 K). Filtering these candidates for those with small band gaps correspondingly yields LiCuF 4 (316 K), Cu 2 P 2 O 7 (311 K), and Cu 3 P 2 H 2 O 9 (307 K).

Abstract of "High- T c superconductor candidates proposed by machine learning" by Siwoo Lee, Jason R. Hattrick-Simpers, Young‐June Kim et al., https://doi.org/10.1088/2632-2153/ae04c1, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.