开发一个完整的AI加速工作流用于超导体发现
Developing a Complete AI-Accelerated Workflow for Superconductor Discovery
AI总结：
本文提出BEE-NET模型，通过预测Eliashberg谱函数和临界温度，高效筛选超导材料，减少候选结构至741种，并实验验证了两种新超导体。
摘要：
寻找具有增强性质的新超导材料受到计算电子-声子谱函数成本的限制。本文引入了Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET)模型，该模型在DFT基的Allen-Dynes计算基础上，以均方误差0.87 K预测Eliashberg谱函数和临界温度。BEE-NET实现了99.4%的真阳性率，通过多阶段AI加速发现流程，结合元素替代策略和机器学习的原子间势能，将130万种候选结构减少至741种动态和热力学稳定的化合物，其中DFT确认的临界温度大于5 K。本文报告了两种此前未报告化合物的成功合成和超导性实验验证，建立了集机器学习、量子计算和实验于一体的驱动数据框架，系统加速超导体发现。
英文摘要：
The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4\%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed $T_{\mathrm{c}} > 5$ K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
