Computational discovery of high-temperature superconducting ternary hydrides via deep learning.

The discovery of novel high-temperature, or even room-temperature, superconducting materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant challenge for both experimental and computational investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be effectively addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal-structure exploration, physics-informed screening and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-[Formula: see text] superconductors with predicted [Formula: see text] K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-[Formula: see text] hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary systems.