We used the superconductors in the SuperCon database to construct element vectors and then perform machine 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 R² ≃ 0.93, which is comparable and in some cases higher than 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 several factors that limit the learning process and suggest possible ways to overcome them.

我们使用SuperCon数据库中的超导体构造元素向量，然后对其临界温度（T _c）进行机器学习。在此过程中仅使用了超导体的化学成分。没有使用任何形式的物理预测指标（既没有实验指标也没有计算指标）。我们实现了判定的系数- [R ² ≃0.93，这与在某些情况下比使用其它人工智能技术类似的估计高。基于此机器学习模型，我们预测了几种具有高临界温度的新型超导体。我们还将讨论限制学习过程的几个因素，并提出克服这些问题的可能方法。