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Autonomous electron-beam fabrication controlled tailored defects in 2D materials

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Research area:biology-genetics

What the study found

The study found that an autonomous, machine learning-enabled scanning transmission electron microscopy system could fabricate atomic-level defects in two-dimensional materials. As a proof of concept, it was used to create MoS-nanowire edge structures in MoS2 monolayers by selectively removing sulfur atoms.

Why the authors say this matters

The authors conclude that the approach is material-agnostic, meaning it is designed to be extended beyond MoS2 to other two-dimensional materials. The study suggests it could be used to create diverse defect structures and heterostructures beyond MoS2.

What the researchers tested

The researchers combined advanced machine learning and automated beam control in scanning transmission electron microscopy (STEM), a technique that uses a focused electron beam to image and modify materials at very small scales. They used high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring, together with a random forest model and a convolutional neural network (CNN) to identify atomic positions and species.

What worked and what didn't

The system could decode HAADF images, identify atomic positions and species, and then use that information in an autonomous decision-making platform to instruct beam control at selected atomic sites. The selected sites were exposed with an FPGA-controlled scan routine, and the result was controlled fabrication of MoS-nanowire edge structures. The abstract does not describe failures or unsuccessful cases.

What to keep in mind

The abstract presents this as a proof of concept, so the described result is limited to the example tested in MoS2 monolayers. It also does not provide detailed performance limits, comparative benchmarks, or failure rates in the available summary.

Key points

  • A fully autonomous fabrication approach was demonstrated for atomic-level defects in two-dimensional materials.
  • The proof-of-concept example created MoS-nanowire edge structures in MoS2 monolayers by selectively ejecting sulfur atoms.
  • Machine learning models were used to interpret HAADF images and identify atomic positions and species.
  • An autonomous platform used that atomic-level information to decide which sites to expose to the electron beam.
  • The authors say the method is material-agnostic and may extend to other two-dimensional materials.

Disclosure

Research title:
Autonomous electron-beam fabrication controlled tailored defects in 2D materials
Authors:
Zijie Wu, Kevin M. Roccapriore, Ayana Ghosh, Kai Xiao, Raymond R. Unocic, Stephen Jesse, Rama K. Vasudevan, Matthew G. Boebinger
Institutions:
North Carolina State University, Oak Ridge National Laboratory, Oak Ridge National Laboratory, Oak Ridge National Laboratory, Oak Ridge National Laboratory, Oak Ridge National Laboratory, Oak Ridge National Laboratory, Oak Ridge National Laboratory, University of Tennessee at Knoxville
Publication date:
2026-04-27
DOI:
10.1038/s41524-026-02025-w
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.

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