IsoME: Streamlining high-precision Eliashberg calculations

This paper introduces the Julia package IsoME, an easy-to-use yet accurate and robust computational tool designed to calculate superconducting properties. Multiple levels of approximation are supported, ranging from the basic McMillan-Allen-Dynes formula and its machine learning-enhanced variant to Eliashberg theory including static Coulomb interactions derived from $GW$ calculations, offering a fully ab initio approach to determine superconducting properties, such as the critical superconducting temperature ($T_\text{c}$) and the superconducting gap function ($\Delta$). We validate IsoME by benchmarking it against various materials, demonstrating its versatility and performance across different theoretical levels. The findings indicate that the previously held assumption that Eliashberg theory overestimates $T_\text{c}$ is no longer valid when $\mu^*$ is appropriately adjusted to account for the finite Matsubara frequency cutoff. Furthermore, we conclude that the constant density of states (DOS) approximation remains accurate in most cases. By unifying multiple approximation schemes within a single framework, IsoME combines first-principles precision with computational efficiency, enabling seamless integration into high-throughput workflows through its $T_\text{c}$ search mode. This makes IsoME a powerful and reliable tool for advancing superconductivity research.