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

• Published in: ACS applied materials & interfaces 2023-06, Vol.15 (25), p.30029-30038
• Main Authors: Zhong, Chengquan, Zhang, Jingzi, Lu, Xiaoting, Zhang, Ke, Liu, Jiakai, Hu, Kailong, Chen, Junjie, Lin, Xi
• Format: Article
• Language: English
• Subjects: chemistry; copper; prediction; superconductivity
• ISSN: 1944-8252; 1944-8252
• DOI: 10.1021/acsami.3c00593

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 Hg₀.₃₇Ba₁.₇₃Ca₁.₁₈Cu₂.₄₁O₆.₉₃Tl₀.₆₉. We expect that such an inverse design model and comprehensive list of potential high Tc superconductors would greatly facilitate future research activities in superconductors.