Superconductivity allows electric conductance with no energy losses when the ambient temperature drops below a critical value (T c ). Currently, the machine learning (ML)-based prediction of potential superconductors has been limited to chemical formulas without explicit treatment of material structures. Herein, we implement an efficient structural descriptor, the smooth overlap of atomic position (SOAP), into the ML models to predict the T c values with explicit atomic structural information. Using a data set containing 5713 compounds, our ML models with the SOAP descriptor achieved a 92.9% prediction accuracy of coefficient of determination (R 2 ) score via rigorous multialgorithm cross-verification procedures, exceeding the 86.3% accuracy record without atomic structure information. Several new high-temperature superconductors with T c values over 90 K were predicted using the SOAP-assisted ML model. This study provides insights into the structure−property relationship of high-temperature superconductors.
Predictive materials design of high-performance alloy electrocatalysts is a grand challenge in hydrogen production via the water electrolysis. The vast combinatorial space of element substitutions in alloy electrocatalysts offers a...
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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