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

arXiv: https://arxiv.org/abs/1908.09102 · HTML: https://mlsanity.com/paper/1908.09102/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Active learning-assisted neutron spectroscopy with log-Gaussian processes](https://mlsanity.com/paper/2209.00980/) (2022)
- [Using Distance Correlation for Efficient Bayesian Optimization](https://mlsanity.com/paper/2102.08993/) (2021)
- [Autonomous microARPES](https://mlsanity.com/paper/2403.13815/) (2024)

## Abstract (arXiv)

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.