Machine learning enabling high-throughput and remote operations at large-scale user facilities

TL;DR

This paper demonstrates how machine learning models can be integrated into large-scale user facility experiments to enable real-time data analysis and feedback, making advanced ML techniques accessible to non-expert users.

Contribution

It provides practical examples and a framework for incorporating machine learning into experimental workflows at synchrotron facilities, lowering barriers for users.

Findings

ML models enable on-the-fly analysis at beamlines

Integration into existing workflows is straightforward

Framework facilitates real-time feedback and data interpretation

Abstract

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.