Machine Learning Accelerated Design of High-Temperature Ternary and Quaternary Nitride Superconductors

The recent advancements in the field of superconductivity have been significantly driven by the development of nitride superconductors, particularly niobium nitride (NbN). Multicomponent nitrides offer a promising platform for achieving high-temperature superconductivity. Beyond their high superconducting transition temperature (Tc), niobium-based compounds are notable for their superior superconducting and mechanical properties, making them suitable for a wide range of device applications. In this work, machine learning is used to identify ternary and quaternary nitrides, which can surpass the properties of binary NbN. Specifically, Nb_0.35Ta_0.23Ti_0.42N shows an 84.95% improvement in Tc compared to base NbN, while the ternary composition Nb_0.55Ti_0.45N exhibits a 17.29% improvement. This research provides a valuable reference for the further exploration of high-temperature superconductors in diversified ternary and quaternary compositions.