Autonomous screening of complex phase spaces using Bayesian optimization for SAXS measurements

The advent of modern, ultrafast X-ray experiments has enabled scientists to probe physical phenomena at an ever smaller scale. However, this has come at a cost of excessive data generation, to the point where current storage and hardware capabilities are routinely surpassed. As such, handling the data efficiently and selectively storing only the information of most relevance is crucial. In this paper, we propose to use Bayesian optimization as a method to alleviate this problem. We apply the method to locate global features in Small Angle X-ray Scattering spectra obtained from conducting experiments with supercritical CO₂. By evaluating the algorithm on more than 250 data points, we show that the implementation is versatile, robust, and computationally efficient, often converging with just a few iterations and with a minimal error penalty. This paves the way for creating fully autonomous experiments, where data science algorithms such as the one presented herein operate hand-in-hand with the expert user to maximize scientific discovery and minimize the associated experimental cost.