Autonomous “closed-loop” experiments are becoming essential at large facilities, but they require reusable software patterns to move data across trusted boundaries, analyze results in real time, and safely steer instruments.

ORNL and the National Science Data Fabric (under the leadership of the University of Tennessee and the University of Utah) are developing and deploying an adaptive strain-mapping workflow for X-ray scattering on wire-arc additive-manufactured parts at the CHESS Structural Materials Beamline. Experimental data stream through the National Science Data Fabric (NSDF) to computing resources, where INTERSECT@ORNL’s Distributed Active Learning (DIAL) builds a surrogate model and recommends the next measurement locations; the beamline executes them, and the loop repeats.

This talk presents the end-to-end architecture, integration points, and potential “gotchas” (latency, metadata, provenance, and operator controls) so others can replicate the pattern for autonomous experiments and AI-ready pipelines across domains.