China University of Mining and Technology Beijing, Beijing, China.
PLoS One. 2021 Aug 6;16(8):e0255823. doi: 10.1371/journal.pone.0255823. eCollection 2021.
Searching for new high temperature superconductors has long been a key research issue. Fe-based superconductors attract researchers' attention due to their high transition temperature, strong irreversibility field, and excellent crystallographic symmetry. By using doping methods and dopant levels, different types of new Fe-based superconductors are synthesized. The transition temperature is a key indicator to measure whether new superconductors are high temperature superconductors. However, the condition for measuring transition temperature are strict, and the measurement process is dangerous. There is a strong relationship between the lattice parameters and the transition temperature of Fe-based superconductors. To avoid the difficulties in measuring transition temperature, in this paper, we adopt a machine learning method to build a model based on the lattice parameters to predict the transition temperature of Fe-based superconductors. The model results are in accordance with available transition temperatures, showing 91.181% accuracy. Therefore, we can use the proposed model to predict unknown transition temperatures of Fe-based superconductors.
寻找新型高温超导体一直是一个关键的研究课题。铁基超导体因其较高的转变温度、较强的不可逆场和优异的晶体对称性而引起了研究人员的关注。通过掺杂方法和掺杂水平，可以合成不同类型的新型铁基超导体。转变温度是衡量新超导体是否为高温超导体的关键指标。然而，测量转变温度的条件非常严格，测量过程也很危险。铁基超导体的晶格参数与转变温度之间存在很强的关系。为了避免测量转变温度的困难，在本文中，我们采用机器学习方法，基于晶格参数建立模型来预测铁基超导体的转变温度。模型结果与现有的转变温度相符，准确率达到 91.181%。因此，我们可以使用提出的模型来预测铁基超导体未知的转变温度。
