  The application of superconducting materials is becoming more and more
widespread. Traditionally, the discovery of new superconducting materials
relies on the experience of experts and a large number of "trial and error"
experiments, which not only increases the cost of experiments but also prolongs
the period of discovering new superconducting materials. In recent years,
machine learning has been increasingly applied to materials science. Based on
this, this manuscript proposes the use of XGBoost model to identify
superconductors; the first application of deep forest model to predict the
critical temperature of superconductors; the first application of deep forest
to predict the band gap of materials; and application of a new sub-network
model to predict the Fermi energy level of materials. Compared with our known
similar literature, all the above algorithms reach state-of-the-art. Finally,
this manuscript uses the above models to search the COD public dataset and
identify 50 candidate superconducting materials with possible critical
temperature greater than 90 K.
