Structural Predictors for Machine Learning Modeling of Superconductivity in Iron-based Materials

Superconductivity in iron-based materials continues to be a focus of intense research effort a decade after its discovery. In particular, the interplay between structure and charge doping as drivers of superconductivity is still a matter of active debate. To address this question we use Machine Learning (ML) approach. Based on published data we created a database covering the available structural information of the 122 family of materials. Using the lattice parameters, pnictogen height and charge doping we trained several ML models designed to predict the critical temperature T_c over the entire parameter space. The analysis of the models suggests that no single variable can fully explain and predict the evolution of T_c, and a combination of at least two parameters are needed. The ML predictions can be used to guide further exploration of the phase diagram of iron-based superconductors.