Accelerating small-angle scattering experiments with simulation-based machine learning
This work addresses efficiency improvements for materials scientists conducting small-angle scattering experiments, though it appears incremental as it builds on existing data-driven optimization approaches.
The paper tackles the problem of optimizing laborious sequential measurements in small-angle neutron scattering experiments by proposing two data-driven methods that iteratively suggest the next measurement target to maximize information gain, and it confirms through numerical simulations that these methods significantly outperform baselines.
Making material experiments more efficient is a high priority for materials scientists who seek to discover new materials with desirable properties. In this paper, we investigate how to optimize the laborious sequential measurements of materials properties with data-driven methods, taking the small-angle neutron scattering (SANS) experiment as a test case. We propose two methods for optimizing sequential data sampling. These methods iteratively suggest the best target for the next measurement by performing a statistical analysis of the already acquired data, so that maximal information is gained at each step of an experiment. We conducted numerical simulations of SANS experiments for virtual materials and confirmed that the proposed methods significantly outperform baselines.