  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.
