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High-Throughput materials modeling based on Density Functional Theory has become very popular recently. If, for example, we search "High-Throughput Perovskites" in Google Scholar, we get over ~14,000 results. Not all are computational and not all are peer-reviewed publications but it shows just how important High Performance Computing is in current materials research. Needless to say, The Materials Project and AFLOW are big players in the High-Throughput Materials Simulation game. There is even a database for Topological Insulators and Weyl Semimetals

However I am not aware of a database or High-Throughput study that is completely focused on the discovery of high $T_C$ superconductors. Can anyone point me toward some references? Are there any studies of this kind at all?

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    $\begingroup$ If Geoff has answered your question you may want to accept their answer. $\endgroup$
    – taciteloquence
    May 26, 2020 at 7:36

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There are several such studies, particularly focusing on the machine-learning of critical temperatures.

They all rely on the "Supercon" database of ~12,000 critical temperatures, which has also been extracted as supporting information for the first paper to GitHub.

In general, the ML methods offer suggestions for new compositions, but I'm not sure if any have yet been attempted.

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    $\begingroup$ Thanks for the answer Geoff! I was sure there had to be some recent studies out there that tackled this problem. I will read them with care, as I confess I do not know how one would go about modeling superconductors, but have always wanted to take a crack at it. $\endgroup$
    – epalos
    May 3, 2020 at 0:24

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