发表机构
Zhongguancun Academy; Zhongguancun Institute of Artificial Intelligence; School of Materials Science and Engineering, Shanghai Jiao Tong University; Kairos Materials; State Key Laboratory of Materials for Advanced Nuclear Energy & School of Materials Science and Engineering, Shanghai University（中关村学院; 中关村人工智能研究院; 上海交通大学材料科学与工程学院; 凯罗斯材料公司; 上海大学先进核能材料国家重点实验室及材料科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了整合组分、结构与离子传输的分层协同深度学习框架，筛选出97种高性能固态电解质候选物，揭示Li⁺跳跃网络连通性是室温离子电导率核心决定因素，为固态电解质发现提供新方法。
AI 中文摘要
无机固态电解质必须同时具备高室温离子电导率、宽电化学窗口、优异的电子绝缘性和良好的机械柔性。单一模型难以在巨大化学空间中支持可靠的多目标筛选，原因在于训练数据分布不匹配、跨属性数据集异质性以及动力学传输数据稀缺。为克服这些局限，我们开发了一种分层协同深度学习框架，通过四个互补模块依次协调效率、准确性和可靠性。自主开发的L-G-DCNN和基于DenseGNN构建的多保真度实现分别作为组分和结构专家，用于热力学粗筛选和多属性评估；MatterSim和特定系统的DeePMD模型提供传输预评估和动力学验证。系统基准测试显示，每个模块在其任务上均优于主流对应模型，而回顾性验证建立了模块级准确性和端到端工作流程可靠性的双闭环验证。将该框架应用于30364908个Alex/ICSD衍生候选物，共识别出97种高性能候选物，其室温离子电导率为0.109至59.0 mS/cm，包括94种卤化物、1种硼氢化物和2种氧化物。与独立实验数据的一致性确认，94种卤化物中的76种处于已报道的高电导率结构区域内。分析表明，Li⁺跳跃网络的连通性而非几何Li位点的数量是室温离子电导率的核心决定因素。Li缺陷工程可有效增强氧化物的传输，而O²⁻框架的固有刚性表明氧化物电解质性能存在潜在上限。
英文摘要
Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulation, and favorable mechanical compliance. Single models struggle to support reliable multi-objective screening across vast chemical spaces because of training-data distribution mismatch, cross-property dataset heterogeneity, and scarce kinetic transport data. To overcome these limitations, we develop a hierarchical synergistic deep-learning framework that sequentially coordinates efficiency, accuracy, and reliability through four complementary modules. The in-house-developed L-G-DCNN and a multi-fidelity implementation built on DenseGNN serve as compositional and structural experts for thermodynamic coarse screening and multi-property evaluation, respectively; MatterSim and system-specific DeePMD models provide transport pre-assessment and kinetic validation. Systematic benchmarks show that each module outperforms mainstream counterparts in its task, while retrospective validation establishes dual closed-loop verification of module-level accuracy and end-to-end workflow reliability. Applied to 30,364,908 Alex/ICSD-derived candidates, the framework identifies 97 high-performance candidates with room-temperature ionic conductivities of 0.109--59.0 mS/cm, including 94 halides, one borohydride, and two oxides. Consistency with independent experimental data confirms that 76 of the 94 halides fall within reported high-conductivity structural regions. Analysis reveals that Li$^{+}$ jump-network connectivity, rather than the number of geometric Li sites, is the core determinant of room-temperature ionic conductivity. Li-defect engineering effectively enhances oxide transport, whereas the inherent rigidity of the O$^{2-}$ framework suggests a potential upper limit on oxide electrolyte performance.
Comments22 pages, 8 figures, 1 table
