Authors: Xiangyu Yin, Chuqiao Shi, Yimo Han, Yi Jiang
Abstract: Ptychography is an advanced computational imaging technique in X-ray and
electron microscopy. It has been widely adopted across scientific research
fields, including physics, chemistry, biology, and materials science, as well
as in industrial applications such as semiconductor characterization. In
practice, obtaining high-quality ptychographic images requires simultaneous
optimization of numerous experimental and algorithmic parameters.
Traditionally, parameter selection often relies on trial and error, leading to
low-throughput workflows and potential human bias. In this work, we develop the
“Ptychographic Experiment and Analysis Robot” (PEAR), a framework that
leverages large language models (LLMs) to automate data analysis in
ptychography. To ensure high robustness and accuracy, PEAR employs multiple LLM
agents for tasks including knowledge retrieval, code generation, parameter
recommendation, and image reasoning. Our study demonstrates that PEAR’s
multi-agent design significantly improves the workflow success rate, even with
smaller open-weight models such as LLaMA 3.1 8B. PEAR also supports various
automation levels and is designed to work with customized local knowledge
bases, ensuring flexibility and adaptability across different research
environments.
Source: http://arxiv.org/abs/2410.09034v1
