Machine learning the relationship between Debye temperature and superconducting transition temperature
Machine learning the relationship between Debye temperature and superconducting transition temperature
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DOI：
10.1103/physrevb.108.174514
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发表时间：
2023-05
影响因子：
3.7
通讯作者：
Adam D. Smith;S. Harris;R. Camata;D. Yan;Cheng-Chien Chen
中科院分区：
文献类型：
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作者：
Adam D. Smith;S. Harris;R. Camata;D. Yan;Cheng-Chien Chen
最近提出了德拜温度$\Theta_D$和常规超导体的超导转变温度$T_c$之间的关系[npj Quantum Materials $\mathbf{3}$，59（2018）]。该关系表明，$T_c \le A \Theta_D$声子介导的BCS超导体，与$A$是一个前因子的顺序$\sim 0.1$。为了验证这个界限，我们用Materials Project数据库中的10，330个样本训练机器学习（ML）模型来预测$\Theta_D$。通过将我们的ML模型应用于NIMS SuperCon数据库中的9，860个已知超导体，我们发现数据库中的传统超导体确实遵循所提出的界限。在200 GPa下，我们还对H${3}$S和LaH${10}$进行了第一性原理声子计算。计算结果表明，这些高压的电子能基本上饱和的界限$T_c$与$\Theta_D$。
Recently a relationship between the Debye temperature $\Theta_D$ and the superconducting transition temperature $T_c$ of conventional superconductors has been proposed [npj Quantum Materials $\mathbf{3}$, 59 (2018)]. The relationship indicates that $T_c \le A \Theta_D$ for phonon-mediated BCS superconductors, with $A$ being a pre-factor of order $\sim 0.1$. In order to verify this bound, we train machine learning (ML) models with 10,330 samples in the Materials Project database to predict $\Theta_D$. By applying our ML models to 9,860 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 H$_{3}$S and LaH$_{10}$ at 200 GPa. The calculation results indicate that these high-pressure hydrides essentially saturate the bound of $T_c$ versus $\Theta_D$.