Machine learning enabling high-throughput and remote operations at large-scale user facilities
This work addresses technical and conceptual barriers for facility users lacking ML expertise, enabling high-throughput and remote operations, though it is incremental as it builds on existing infrastructure like the Bluesky Suite.
The authors tackled the challenge of integrating machine learning into experimental workflows at large-scale user facilities, demonstrating that diverse ML models can be easily deployed for on-the-fly analysis at multiple beamlines, such as at NSLS-II, with minimal effort.
Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.