Deep Generative Model for Inverse Design of High-Temperature Superconductor Compositions with Predicted T_c > 77 K

Abstract

Identifying new superconductors with high transition temperatures (T c > 77 K) is a major goal in modern condensed matter physics. The inverse design of high T c superconductors relies heavily on an effective representation of the superconductor hyperspace due to the underlying complexity involving many-body physics, doping chemistry and materials, and defect structures. In this study, we propose a deep generative model that combines two widely used machine learning algorithms, namely, the variational auto-encoder (VAE) and the generative adversarial network (GAN), to systematically generate unknown superconductors under the given high T c condition. After training, we successfully identified the distribution of the representative hyperspace of superconductors with different T c , in which many superconductor constituent elements were found adjacent to each other with their neighbors in the periodic table. Equipped with the conditional distribution of T c , our deep generative model predicted hundreds of superconductors with T c > 77 K, as predicted by the published T c prediction models in the literature. For the copper-based superconductors, our results reproduced the variation in Tc as a function of the Cu concentration and predicted an optimal T c = 129.4 K, when the Cu concentration reached 2.41 in Hg 0.37 Ba 1.73 Ca 1.18 Cu 2.41 O 6.93 Tl 0.69 . We expect that such an inverse design model and comprehensive list of potential high Tc superconductors would greatly facilitate future research activities in superconductors.

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