Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (T_c) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their T_c values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of T_c for cuprate, iron-based, and low-T _c compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify >30 non-cuprate and non-iron-based oxides as candidate materials.

自一个多世纪以来发现超导以来，它一直是大量研究工作的重点。然而，这种独特现象的某些特征仍然知之甚少。其中最主要的是超导性与材料化学/结构性质之间的联系。为了弥合差距，本文开发了几种机器学习方案，以对通过SuperCon数据库可获得的12,000多种已知超导体的临界温度（T _c）进行建模。首先根据材料的T _c将其分为两类值（高于和低于10 K），以及训练预测此标签的分类模型。该模型仅基于化学成分使用粗粒度特征。它显示出强大的预测能力，样本外准确度约为92％。单独的回归模型来预测值牛逼_Ç的铜氧化物，铁基，和低牛逼_Ç 化合物。这些模型还显示出良好的性能，学得好的预测因子可提供对不同材料系列中超导性背后机制的潜在见解。为了提高这些模型的准确性和可解释性，使用AFLOW在线存储库中的材料数据合并了新功能。最后，将分类和回归模型组合到一个单一的集成管道中，并用于在整个无机晶体结构数据库（ICSD）中搜索潜在的新超导体。我们确定了> 30种非铜酸盐和非铁基氧化物作为候选材料。