npj: 超导转变温度——机器学习作预测
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自一个多世纪以前被发现以来，超导便成为材料和物理领域中最令人着迷的课题，吸引了无数聪明的头脑。然而，超导现象中的很多问题至今仍不清楚，其中超导特性与材料的化学组分/结构之间的关系是一个主要的问题。
针对这一问题，一个由马里兰大学帕克校区Valentin Stanev教授领导、杜克大学及美国国家标准局研究人员参与的研究团队开发了几种机器学习方案，对超过12,000种已知超导体和候选材料的超导转变温度（Tc）进行建模。他们首先基于化学组分训练了一个分类模型，以Tc是高于还是低于10 K为标准对已知的超导体进行分类，得到了较为准确的结果。继而开发了回归模型来预测铜基、铁基和低转变温度化合物等各种材料的Tc 值，同样取得了较好结果，同时学习中建立的预测因子可以为揭示不同材料体系中的超导机理提供线索。利用AFLOW在线存储库（Online Repositories）中的材料数据，他们进一步提高了这些模型的准确性。最后，他们将分类和回归模型组合成一个集成管道，应用其搜索了整个无机晶体结构数据库并预测出30多种新的潜在超导体。
该文近期发表于npj Computational Materials, 4: 29 (2018)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
Machine learning modeling of superconducting critical temperature
Valentin Stanev, Corey Oses, A. Gilad Kusne, Efrain Rodriguez, Johnpierre Paglione, Stefano Curtarolo & Ichiro Takeuchi
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 (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc 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 Tc for cuprate, iron-based, and low-Tc 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.
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