A critical technological need will be to bridge the increasingly high-temperature performance with ambient pressure . A useful tool to accomplish this goal of accelerated superconductive materials discovery is through machine learning, where there has been various implementations. For example, superconducting phase diagrams were predicted using text mining , superconducting hydrogen compounds were found using a genetic algorithm and genetic programming , critical temperature and pressure were predicted for hydrides , critical temperatures of doped Fe-based superconductors were predicted based on structural and topological parameters , and critical temperature was predicted on a structure based model using a structural descriptor , and superconductor materials and properties have been automatically extracted from literature . An ML-guided discovery will hopefully replace the "serendipitous discovery paradigm" that has existed in this last century of superconductor research .