Abstract
This work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved small-angle neutron scattering using early-time data. Built on a Gaussian process regression framework, the method reconstructs scattering profiles with quantified uncertainty, even from sparse or noisy measurements. Demonstrated on the EQ-SANS instrument at the Spallation Neutron Source, the approach generalizes to general SANS instruments with a two-dimensional detector. A key result is the discovery of a dimensionless convergence metric revealing a universal power-law scaling in profile evolution across soft matter systems. When time is normalized by a system-specific characteristic time t*, the variation in inferred profiles collapses onto a single curve with an exponent between −2 and −1. This trend emerges within the first ten time steps, enabling early prediction of measurement sufficiency. The method supports real-time experimental optimization and is especially valuable for maximizing efficiency in low-flux environments such as compact accelerator-based neutron sources.
| Original language | English |
|---|---|
| Article number | 074107 |
| Journal | Journal of Chemical Physics |
| Volume | 163 |
| Issue number | 7 |
| DOIs | |
| State | Published - Aug 21 2025 |
Funding
We extend our sincere gratitude to Sidney Yip, Gilbert Strang, James S. Langer, Thomas Gutberlet, and Chun-Keung Loong for their insightful communications. This research at ORNL’s Spallation Neutron Source was sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy. This research was also supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. Beam time was allocated to EQ-SANS under Proposal Nos. IPTS-22170.1, 22386.1, 23463.1, and 25953.1. A portion of this research was performed at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility operated by Oak Ridge National Laboratory. Y.S. was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC05-00OR22725. G.R.H. was supported by the National Science and Technology Council (NSTC) in Taiwan under Grant Nos. NSTC 111-2112-M-110-021-MY3, NSTC 113-2112-M-029-007, and NSTC 114-2628-M-007-004-MY4. This research was sponsored by the U.S. Department of Energy Office of Science, Office of Basic Energy Sciences, Data, Artificial Intelligence and Machine Learning at DOE Scientific UserFacilities Program under Award No. 34532.