Abstract: 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 the chemical and structural properties of materials. Machine learning is a new tool that can help us bridge this gap.
I will present several recently developed machine learning methods for modeling the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database. These models use coarse-grained predictors based only on the chemical composition of the materials. They demonstrate good performance and strong predictive power, with learned predictors offering insights into the mechanisms behind superconductivity in different families. The models can be combined into a single pipeline and employed to search for potential new superconductors. Searching the entire Inorganic Crystallographic Structure Database led to the identification of 35 compounds as candidate high-Tc materials.