Predicting new superconductors and their critical temperatures using machine learning

被引：48

作者：

Roter, B. ^{[1
]}

Dordevic, S., V ^{[1
]}

机构：

[1] Univ Akron, Dept Phys, Akron, OH 44325 USA

来源：

PHYSICA C-SUPERCONDUCTIVITY AND ITS APPLICATIONS | 2020年 / 575卷

关键词：

Machine learning; High temperature superconductors;

D O I：

10.1016/j.physc.2020.1353689

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

We used the superconductors in the SuperCon database to construct element vectors and then perform machine learning of their critical temperatures (T-c). Only the chemical composition of superconductors was used in this procedure. No physical predictors (neither experimental nor computational) of any kind were used. We achieved the coefficient of determination R-2 similar or equal to 0.93, which is comparable and in some cases higher than similar estimates using other artificial intelligence techniques. Based on this machine learning model, we predicted several new superconductors with high critical temperatures. We also discuss several factors that limit the learning process and suggest possible ways to overcome them.

引用

页数：4

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