Mapping superconductivity in high-pressure hydrides: The Superhydra project

The discovery of high-${T}_{c}$ conventional superconductivity in high-pressure hydrides has helped establish computational methods as a formidable tool to guide material discoveries in a field traditionally dominated by serendipitous experimental search. This paves the way to an ever-increasing use of data-driven approaches to the study and design of superconductors. In this work, we propose a new adaptive method to generate meaningful datasets of superconductors, based on element substitution into a small set of representative structural templates, generated by crystal structure prediction methods---adapted high-throughput approach. Our approach realizes an optimal compromise between structural variety and computational efficiency and can be easily generalized to other elements and compositions. As a first application, we apply it to binary hydrides at high pressure, realizing a database of 880 hypothetical structures, characterized with a set of electronic, vibrational, and chemical descriptors. In our Superhydra Database, 139 structures are superconducting according to the McMillan-Allen-Dynes approximation. Studying the distribution of ${T}_{c}$ and other properties across the database with advanced statistical and visualization techniques, we are able to obtain comprehensive material maps of the phase space of binary hydrides. The Superhydra database can be thought as a first step of a generalized effort to map conventional superconductivity.