发表机构
Fudan University; Shanghai Research Center for Quantum Sciences; Hefei National Laboratory（复旦大学; 上海量子科学研究中心; 合肥国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过可解释机器学习，揭示铜基与铁基超导体在材料空间的共同特征，结合$T_{\text{c}}$模型实现超导体候选材料的优先级排序，复现镍酸盐超导体并识别新候选。
AI 中文摘要
超出声子介导机制的高临界温度超导体通常被认为具有非常规本质，但与常规超导体不同，目前尚无广泛适用的预测理论指导其发现。本研究使用可解释机器学习来揭示高$T_{\text{c}}$非常规超导体的共同材料空间特征，并开发数据驱动的材料发现策略。我们通过整合成分统计、结构信息以及训练后的性能预测模型的潜在表示，为每种材料构建统一的特征表示，随后对实验建立的超导体数据集进行感知结构的过滤。在不使用转变温度信息的情况下，无监督分析显示铜基和铁基超导体占据材料空间的共同区域，其主要特征为大的电负性偏差和中等平均价电子数。有监督的$T_{\text{c}}$模型独立识别出相同的描述符作为主导特征，为其相关性提供补充证据。利用该经验材料空间先验结合$T_{\text{c}}$模型，我们对候选材料进行优先级排序，成功复现了近期发现的镍酸盐超导体，并识别出化学性质不同的候选材料以供后续研究。
英文摘要
Superconductors with high critical temperatures that emerges beyond the phonon-mediated regime are usually considered unconventional in nature, yet unlike conventional superconductors, no broadly applicable predictive theory currently guides their discovery. Here, we use interpretable machine learning to uncover a common materials-space signature of high-$T_{\mathrm{c}}$ unconventional superconductors and develop a data-driven strategy for materials discovery. We construct a unified feature representation for each material by integrating compositional statistics, structural information, and latent representations from trained property-prediction models, followed by structure-aware filtering of an experimentally established superconducting dataset. Without using transition-temperature information, unsupervised analysis shows that cuprate and iron-based superconductors occupy a common region of materials space, characterized primarily by large electronegativity deviation and intermediate mean valence-electron number. A supervised $T_{\text{c}}$ model independently identifies the same descriptors as dominant features, providing complementary evidence for their relevance. Using this empirical materials-space prior together with the $T_{\text{c}}$ model, we prioritize candidate materials, recover recently discovered nickelate superconductors, and identify chemically distinct candidates for future investigation.
Comments12 pages, 5 figures
