Automatic parameter selection for electron ptychography via Bayesian optimization.

Area of Science:

• Materials Science
• Microscopy
• Computational Science

Background:

• Electron ptychography offers high-resolution atomic structure imaging and efficient dose usage for sensitive materials.
• Current methods rely on manual, trial-and-error parameter optimization, hindering throughput and adoption.
• Automated parameter selection is crucial for advancing electron ptychography applications.

Purpose of the Study:

• To develop an automated framework for parameter selection in electron ptychography.
• To improve the accuracy and efficiency of ptychographic reconstructions.
• To facilitate wider adoption and better experimental design in electron microscopy.

Main Methods:

• Bayesian optimization with Gaussian processes was employed for automated parameter selection.
• The framework requires minimal prior experimental knowledge.
• Simulated data was used to explore and optimize experimental parameters.

Main Results:

• The automated workflow produced superior ptychographic reconstructions compared to expert manual optimization.
• The method significantly improved experimental throughput.
• The framework demonstrated efficient exploration of optimal experimental parameters.

Conclusions:

• Automated parameter selection via Bayesian optimization enhances electron ptychography performance.
• This approach overcomes limitations of manual tuning, enabling routine high-resolution imaging.
• The developed framework accelerates scientific discovery by optimizing experimental designs and data acquisition.