论文标题
使用无监督的机器学习聚类超导体
Clustering Superconductors Using Unsupervised Machine Learning
论文作者
论文摘要
在这项工作中，我们使用了无监督的机器学习方法，以便在超导材料数据集中找到可能的聚类结构。我们使用了SuperCON数据库以及根据文献遵守的自己的数据集，以探讨机器学习算法如何分组超导体。使用了诸如K-均值，层次或高斯混合物的常规聚类方法，以及基于人工神经网络（如自组织图）的聚类方法。为了降低维度，发现t-sne是最佳选择。我们的结果表明，机器学习技术可以实现，在某些情况下可以超过人类水平的表现。计算表明，当机器学习技术与人类的超导体知识一起使用时，超导材料的聚类效果最好。我们还表明，为了解决数据中的良好亚集群结构，应分阶段进行超导材料的聚类。
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.
