论文标题
使用无监督的机器学习预测新的超导体及其临界温度
Predicting new superconductors and their critical temperatures using unsupervised machine learning
论文作者
论文摘要
我们使用SuperCon数据库中的超导体来构建元素向量，然后对其临界温度（T $ _C $）进行无监督的学习。在此过程中，仅使用超导体的化学组成。没有使用任何类型的物理预测因素（既没有实验性的）。我们实现了确定系数r $^2 $$ \ simeq $ 0.93，它是可比的，在某些情况下，使用其他人工智能技术较高的估计。基于这种机器学习模型，我们预测了几个新的超导体，具有较高的临界温度。我们还讨论了限制学习过程的因素，并提出了克服它们的可能方法。
We used the superconductors in the SuperCon database to construct element vectors and then perform unsupervised learning of their critical temperatures (T$_c$). Only the chemical composition of superconductors was used in this procedure. No physical predictors (neither experimental nor computational) of any kind were used. We achieved the coefficient of determination R$^2$$\simeq$0.93, which is comparable and in some cases higher then similar estimates using other artificial intelligence techniques. Based on this machine learning model, we predicted several new superconductors with high critical temperatures. We also discuss the factors that limit the learning process and suggest possible ways to overcome them.
