Machine learning modeling of superconducting critical temperature

Machine learning has emerged as a powerful new research tool that can be used to answer many scientific questions in unconventional ways. In this talk I will discuss how it can help us address one of the most challenging problems in the study of quantum matter – finding connection between superconductivity – in particular critical temperature T_c – and chemical/structural properties of materials. I will present several recently developed machine learning methods for modeling T_c 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-T_c materials. I will also discuss how machine learning can be used to guide and accelerate the experimental process.