Designing Superconductors with Periodic Table-based Maps and Material Databases

One of the grand challenges of superconductivity science is achieving a paradigm shift from discovery by serendipity to discovery by design. Empirical and heuristic rules have been a useful bridge in this desired direction. Many early superconductors were discovered by this method and by seredipity. DFT-based \textit{ab initio} methods have often ignored empirical and experimental data. Here we propose that by using Periodic Table-based maps such as electronegativity spectrum maps, valence electron spectrum maps and atomic number spectrum maps for binary systems, A$_{x}$B$_{y}$, combined with data-mining of experimental material databases we can ``reverse-engineer'' many known superconductors. We demonstrate the power of this technique by predicting new and novel superconductors without recourse to DFT calculations.