In this work we used unsupervised machine learning methods in order to find possible clustering structures in superconducting materials data sets. We used the SuperCon database, as well as our own data sets complied from literature, in order to explore how machine learning algorithms groups superconductors. Both conventional clustering methods like k-means, hierarchical or Gaussian mixtures, as well as clustering methods based on artificial neural networks like self-organizing maps, were used. For dimensionality reduction and visualization t-SNE was found to be the best choice. Our results indicate that machine learning techniques can achieve, and in some cases exceed, human level performance. Calculations suggest that the clustering of superconducting materials works best when machine learning techniques are used in concert with human knowledge of superconductors. We also show that in order to resolve fine subcluster structure in the data, clustering of superconducting materials should be done in stages.

在这项工作中，我们使用了无监督机器学习方法，以便在超导材料数据集中找到可能的聚类结构。我们使用 SuperCon 数据库以及我们自己的文献数据集，以探索机器学习算法如何对超导体进行分组。使用了传统的聚类方法，如 k-means、分层或高斯混合，以及基于人工神经网络的聚类方法，如自组织图。对于降维和可视化，发现 t-SNE 是最佳选择。我们的结果表明，机器学习技术可以达到甚至在某些情况下超过人类水平的表现。计算表明，当机器学习技术与人类对超导体的知识结合使用时，超导材料的聚类效果最好。我们还表明，为了解决数据中精细的亚团结构，超导材料的聚类应该分阶段进行。