Dalian researchers build world's first AI-driven transmission electron microscope

Chinese scientists built an AI-controlled electron microscope, Aeye-1, that runs sample transfer, imaging, and analysis without human input. It works 300 times faster than manual analysis and processed 168 samples daily in tests.

Categorized in: AI News Science and Research
Published on: May 26, 2026
Dalian researchers build world's first AI-driven transmission electron microscope

Chinese Researchers Build World's First AI-Driven Electron Microscope

Scientists at the Dalian Institute of Chemical Physics have developed an artificial intelligence-driven transmission electron microscope system called "Aeye-1," shifting microscopy from manual operation to fully autonomous AI control. The system passed a scientific evaluation on May 26 organized by the China Petroleum and Chemical Industry Federation, with the evaluation committee concluding it is the first of its kind globally.

The microscope handles the entire workflow automatically: sample transfer, imaging, and analysis. "Like a 'smart eye' visualizing the atomic world, the system enables fully unmanned, AI operation across the entire workflow," said Deng Dehui, a professor leading the project.

Speed and Scale

Aeye-1 processes images 300 times faster than manual analysis. Two weeks of operation produced data equivalent to one year of work on a conventional microscope.

In tests analyzing molecular sieves catalysts, the system processed 168 samples daily and captured more than 4,000 images per day. It automatically generated professional analytical reports with quantitative microstructural statistics.

Research Applications

The technology targets energy and chemical engineering, advanced materials, and life sciences. Researchers expect Aeye-1 to supply large-scale, high-quality structural data that could reshape how scientific teams approach microscopy work.

For professionals managing research operations, this represents a shift toward AI-driven workflows in laboratory settings. Understanding how these systems integrate into existing research practices is increasingly relevant.

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