5 papers
EAA: Automating materials characterization with vision language model agents
Ming Du, Yanqi Luo, Srutarshi Banerjee +3
We present Experiment Automation Agents (EAA), a vision-language-model-driven agentic system designed to automate complex experimental microscopy workflows. EAA integrates multimod…
Optimizing Paths for Adaptive Fly-Scan Microscopy: An Extended Version
Yu Lu, Thomas F. Lynn, Ming Du +2
In x-ray microscopy, traditional raster-scanning techniques are used to acquire a microscopic image in a series of step-scans. Alternatively, scanning the x-ray probe along a conti…
Fidelity-preserving enhancement of ptychography with foundational text-to-image models
Ming Du, Volker Rose, Junjing Deng +3
Ptychographic phase retrieval enables high-resolution imaging of complex samples but often suffers from artifacts such as grid pathology and multislice crosstalk, which degrade rec…
DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis
Aileen Luo, Tao Zhou, Ming Du +3
Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experime…
Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy
Ming Du, Mark Wolfman, Chengjun Sun +2
X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but re…