AI-Assisted Synchrotron X-Ray Scattering Experimentation

The extraordinarily high flux at synchrotron light sources continue to enable versatile in-situ and autonomous experimentation for the invention and development of functional nanomaterials. Synchrotron beamlines are however constantly oversubscribed, efficient use of beamtime and sustainable beamline operation are thus essential. X-ray scattering beamlines often adopt various customized or in-situ setups, integration to beamline and dexterous control of each component are critical for smooth and efficient experimentation. Advances in artificial intelligence and machine learning, including natural language processing methods, can be utilized for effective and sustainable beamline operation, focused scientific discovery, human-AI integration, as well as user support and education.