用AI+电镜解析二维材料缺陷三维分布，助力精准设计功能材料。
Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes
- 结合AI与电子显微技术，重建百万级晶格点的缺陷三维坐标。
- 发现缺陷从孤立空位到纳米孔的分级结构，揭示其形成机制。
- 结果可关联合成路径，适合材料设计与缺陷工程研究者。
点缺陷决定二维材料诸多重要功能特性，但多层二维材料中缺陷的三维排布仍难解析，制约缺陷理性设计。本文通过人工智能引导的电子显微流程，实现了对Ti₃C₂T_X MXene中原子空位的三维拓扑与聚类行为的映射。该方法重建了数十万晶格点的缺陷三维坐标，获得稳健的统计分布信息，可与特定合成路径关联。大规模数据使我们分类出缺陷结构层级——从孤立空位到纳米孔，并揭示其偏好形成与相互作用机制，分子动力学模拟予以验证。本工作提供了一种可推广的缺陷理解与控制框架，为大面积缺陷工程功能性二维材料的理性设计铺平道路。
原文摘要 · Abstract (English)
Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti$_3$C$_2$T$_X$ MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures--from isolated vacancies to nanopores--revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.
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