学术文献库

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 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 (MultiTemplate-HighThroughput 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. 139 structures of our $Superhydra$ Database 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.