Accelerating the Search for Superconductors Using Machine Learning

Prediction of critical temperature $(T_c)$ of a superconductor remains a significant challenge in condensed matter physics. While the BCS theory explains superconductivity in conventional superconductors, there is no framework to predict $T_c$ of unconventional, higher $T_{c}$ superconductors. Quantum Structure Diagrams (QSD) were successful in establishing structure-property relationship for superconductors, quasicrystals, and ferroelectric materials starting from chemical composition. Building on the QSD ideas, we demonstrate that the principal component analysis of superconductivity data uncovers the clustering of various classes of superconductors. We use machine learning analysis and cleaned databases of superconductors to develop predictive models of $T_c$ of a superconductor using its chemical composition. Earlier studies relied on datasets with inconsistencies, leading to suboptimal predictions. To address this, we introduce a data-cleaning workflow to enhance the statistical quality of superconducting databases by eliminating redundancies and resolving inconsistencies. With this improvised database, we apply a supervised machine learning framework and develop a Random Forest model to predict superconductivity and $T_c$ as a function of descriptors motivated from Quantum Structure Diagrams. We demonstrate that this model generalizes effectively in reasonably accurate prediction of $T_{c}$ of compounds outside the database. We further employ our model to systematically screen materials across materials databases as well as various chemically plausible combinations of elements and predict $\mathrm{Tl}_{5}\mathrm{Ba}_{6}\mathrm{Ca}_{6}\mathrm{Cu}_{9}\mathrm{O}_{29}$ to exhibit superconductivity with a $T_{c}$ $\sim$ 105 K. Being based on the descriptors used in QSD's, our model bypasses structural information and predicts $T_{c}$ merely from the chemical composition.