  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 ($T_{\mathrm{c}}$) of the 12,000+ known superconductors
available via the SuperCon database. Materials are first divided into two
classes based on their $T_{\mathrm{c}}$ 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 $T_{\mathrm{c}}$ for
cuprate, iron-based, and "low-$T_{\mathrm{c}}$" 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 more than 30 non-cuprate and non-iron-based oxides as candidate
materials.
