Speaker
Description
NbN is a workhorse superconductor in quantum devices, detectors, and high-field applications, yet a quantitative microscopic description of its structure and superconducting behavior remains incomplete. Predictions of the critical temperature ($T_\text{c}$) are systematically higher than experimental values, indicating missing ingredients in current models. Open questions remain regarding the role of nitrogen vacancies, disorder, low dimensionality, and anharmonicity, and how these factors influence stability, the Eliashberg spectral function $\alpha^2F(\omega)$, and ultimately $T_\text{c}$. Addressing these challenges requires going beyond state-of-the-art approaches, including the use of machine learning potentials to overcome computational limitations.
By examining how subtle structural and dynamical effects shape superconducting properties, this work on NbN bridges first-principles calculations with experimental observables and enables a predictive framework for related superconducting materials.