Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learning
Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learning
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使用机器学习根据结构和拓扑参数预测掺杂铁基超导体临界温度
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Xiaojie Xu
中科院分区：
文献类型：
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作者：
Yun Zhang;Xiaojie Xu
摘要近年来，铁基超导体表现出高临界温度和高上临界场的优良特性，这是铁基超导体在强场磁体中应用的先决条件。临界温度Tc是与晶体学和电子结构相关的重要特性。通过在晶体结构中掺杂杂质离子，可以改变Tc，然而这需要大量的人力和资源用于材料合成和表征。在这项研究中，我们开发了高斯过程回归模型来预测掺杂的铁基超导体的Tc的结构和拓扑参数的基础上，包括晶格常数，体积，和键参数拓扑指数H31。该模型稳定且准确，有助于快速估算Tc。
Abstract Recently, Fe-based superconductors have shown promising properties of high critical temperature and high upper critical fields, which are prerequisites for applications in high-field magnets. Critical temperature, Tc, is an important characteristic correlated with crystallographic and electronic structures. By doping with foreign ions in the crystal structure, Tc can be modified, which however requires significant manpower and resources for materials synthesis and characterizations. In this study, we develop the Gaussian process regression model to predict Tc of doped Fe-based superconductors based on structural and topological parameters, including the lattice constants, volume, and bonding parameter topological index H31. The model is stable and accurate, contributing to fast Tc estimations.