Enhancing Superconductor Critical Temperature Prediction: A Novel Machine Learning Approach Integrating Dopant Recognition

Doping plays a crucial role in determining the critical temperature (<i>T</i>_c) of superconductors, yet accurately predicting its effects remains a significant challenge. Here, we introduce a novel doping descriptor that captures the complex influence of dopants on superconductivity. By integrating the doping descriptor with elemental and physical features within a Mixture of Experts (MoE) model, we achieve a remarkable <i>R</i>² of 0.962 for <i>T</i>_c prediction, surpassing all published prediction models. Our approach successfully identifies optimal doping levels in the Bi_2-<i>x</i>Pb_<i>x</i>Sr₂Ca_2-<i>y</i>Cu_<i>y</i>O_<i>z</i> system, with predictions closely aligning with experimental results. Leveraging this model, we screen compounds from the Inorganic Crystal Structure Database and employ a generative approach to explore new doped superconductors. This process reveals 40 promising candidates for high <i>T</i>_c superconductivity among existing and hypothetical doped materials. By explicitly accounting for doping effects, our method offers a powerful tool for guiding the experimental discovery of new superconductors, potentially accelerating progress in high-temperature superconductivity research and opening new avenues for material design.