Towards in silico mining for superconductors – Cutting the Gordian knot

Abstract

Utilizing the random forest model, feasibility of training machine learning regressor models to predict critical temperatures of superconductivity from Density Functional Theory (DFT) based electronic band structures is explored. This complementarity between experiment and theory draws inspiration from the merging of Kohn-Sham and Bogoliubov-De Gennes equations [W. Kohn, W, EKU Gross, and LN Oliveira, Int. J. of Quant. Chem.,36(23), 611–615 (1989)]. Features in the Kohn-Sham Density Functional Theory band structure away from EF becoming decisive for the superconducting gap demonstrates this divide-and-conquer physical understanding. Not committing to any microscopic mechanism for the SC at this stage, it implies that in different classes of materials, different electronic features are responsible for the superconductivity. However, training on known members of a class, the performance of new members may be predicted. The method is validated for the A15 materials, including both binary A3X and ternary A6XY intermetallics, A = V, Nb, demonstrating that the two do indeed belong to the same class of superconductors.