# Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

arXiv: https://arxiv.org/abs/2511.08350 · HTML: https://mlsanity.com/paper/2511.08350/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 62 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond](https://mlsanity.com/paper/2105.07485/) (2021)
- [An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics](https://mlsanity.com/paper/2109.14772/) (2021)
- [Image registration of low signal-to-noise cryo-STEM data](https://mlsanity.com/paper/1710.09281/) (2017)
- [SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation](https://mlsanity.com/paper/2404.06638/) (2024)
- [Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision](https://mlsanity.com/paper/2411.09896/) (2024)

## Built on by (most cited first, 1 of 1)

- [Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials](https://mlsanity.com/paper/2605.00639/) (2026)

## Abstract (arXiv)

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 Ti3C2TX 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.