Data-Driven Transition Temperature Enhancement of NbN Layered Structures: A Framework for Quantum Materials Design

Abstract

NbN has become an important material for superconducting applications, largely due to its ability to have a high superconducting transition temperature (T_C) compared with other transition metal nitrides. However, there is still a need to enhance the T_C further as well as to develop a framework for rapidly scanning the material design space and fine-tuning the processing parameters for optimized properties. The unique integrated approach developed and applied here enhances NbN T_C through an ensemble modeling approach to develop general design rules coupling categorical and quantitative data with an optimization approach for fine-tuning the film thickness, substrate chemistry, and processing parameters. As experimental data are used to build the models, the effects of defects are inherently captured. Through this unique framework, a combination of controllable factors are identified which lead to a calculated increase of 16.4% in the T_C of the NbN film as compared with the best NbN film using the same deposition methods.