Accelerating small-angle scattering experiments with simulation-based machine learning

TL;DR

This paper introduces data-driven, simulation-based machine learning methods to optimize sequential small-angle neutron scattering experiments, significantly improving efficiency in material property measurements.

Contribution

It presents two novel methods for optimizing sequential data sampling in SANS experiments using statistical analysis of existing data.

Findings

Proposed methods outperform baseline sampling strategies.

Numerical simulations confirm increased efficiency in data collection.

Methods effectively guide the next measurement to maximize information gain.

Abstract

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.