| Abstract Scope |
The capabilities provided by next generation light sources along with the development of new characterization techniques and detector advances are revolutionizing materials characterization by providing the ability to perform scale-bridging, multi-modal materials characterization under in-situ and operando conditions. For example, providing the ability to image in 3D large fields of view (~mm3) at high resolution (~10 nm), while simultaneously acquiring information about structure, strain, elemental composition, oxidation state, photovoltaic response etc. However, these novel capabilities dramatically increase the complexity and volume of data generated by instruments. Conventional data processing and analysis methodologies become infeasible in the face of such large and varied data streams. The use of AI/ML methods is becoming indispensable for real-time analysis, data abstraction and decision making at advanced light sources. I will describe the use of high-performance computing (HPC) along with AI on edge devices to enable real-time analysis and autonomous experimentation on x-ray imaging instruments. |