Deep Learning Based Superconductivity Prediction and Experimental Tests

The discovery of novel superconducting materials is a longstandingchallenge in materials science, with a wealth of potential forapplications in energy, transportation, and computing. Recentadvances in artificial intelligence (AI) have enabledexpediting the search for new materials byefficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized the compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction.Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge of the chemical properties of the compound, while our neural net inputs depend solely on the chemical composition. We further discuss the existing limitations and challenges associated with using AI to predict and discover new superconductors, along with potential future research directions.