Machine learning the relationship between Debye temperature and superconducting transition temperature

Adam D. Smith, Sumner B. Harris, Renato P. Camata, Da Yan, Cheng Chien Chen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Recently a relationship between the Debye temperature ΘD and the superconducting transition temperature Tc of conventional superconductors has been proposed [Esterlis, npj Quantum Mater. 3, 59 (2018)2397-464810.1038/s41535-018-0133-0]. The relationship indicates that Tc≤AΘD for phonon-mediated BCS superconductors, with A being a prefactor of order ∼0.1. In order to verify this bound, we train machine learning (ML) models with 10 330 samples in the Materials Project database to predict ΘD. By applying our ML models to 9860 known superconductors in the NIMS SuperCon database, we find that the conventional superconductors in the database indeed follow the proposed bound. We also perform first-principles phonon calculations for H3S and LaH10 at 200 GPa. The calculation results indicate that these high-pressure hydrides essentially saturate the bound of Tc versus ΘD.

Original languageEnglish
Article number174514
JournalPhysical Review B
Volume108
Issue number17
DOIs
StatePublished - Nov 1 2023

Funding

The authors thank Steven Kivelson for fruitful discussion. A.D.S., R.P.C., and C.-C.C. are support by the National Science Foundation (NSF) RII Track-1 Future Technologies and Enabling Plasma Processes (FTPP) Project No. OIA-2148653. A.D.S acknowledges support from the UAB Blazer Fellowship and the FTPP CERIF Graduate Research Assistantship. C.-C.C. is also supported by NSF Award No. DMR-2142801. S.B.H. is supported by the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. D.Y. acknowledges support from ARDEF 1ARDEF21 03 from ADECA and NSF Award No. OAC-2106461. Part of the calculations were performed on the Frontera computing system at the Texas Advanced Computing Center. Frontera is made possible by NSF Award No. OAC-1818253.

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