Benchmarking Autonomous Scattering Experiments Illustrated on TAS

Abstract

With the advancement of artificial intelligence and machine learning methods, autonomous approaches are recognized to have great potential for performing more efficient scattering experiments. In our view, it is crucial for such approaches to provide thorough evidence about respective performance improvements in order to increase acceptance within a scientific community. Therefore, we propose a benchmarking procedure designed as a cost-benefit analysis that is applicable to any scattering method sequentially collecting data during an experiment. For a given approach, the performance assessment is based on how much benefit, given a certain cost budget, it is able to acquire in predefined test cases. Different approaches thus get a chance for comparison and can make their advantages explicit and visible. Key components of the procedure, i.e., cost measures, benefit measures, and test cases, are made precise for the setting of three-axes spectrometry (TAS) as an illustration. Finally, we discuss neglected aspects and possible extensions for the TAS setting and comment on the procedure’s applicability to other scattering methods. A Python implementation of the procedure to simplify its utilization by interested researchers from the field is also provided.

Active learning-assisted neutron spectroscopy with log-Gaussian processes

“…We briefly describe its setting in the following paragraph. For more details, we refer to the original work 34 .…”

“…The benchmark results can be reproduced by using code from the repository jugit.fz-juelich.de/ainx/base-fork-ariane (commit SHA: 3715a772 ). It is a fork, adjusted to our approach, of the benchmark API from jugit.fz-juelich.de/ainx/base which is part of the mentioned original work on the benchmarking procedure 34 .…”

AutoRefl: active learning in neutron reflectometry for fast data acquisition

“…Higher measurement efficiency will lead to shorter measurement times and opportunities for additional experiments. Improving the speed of X-ray and neutron measurements with AL (Noack et al, 2019(Noack et al, , 2020McDannald et al, 2022;Teixeira Parente et al, 2023) is thus very much needed.…”

Adaptive sampling for accelerating neutron diffraction-based strain mapping *

“…Alternate approaches, such as Bayesian Optimization (BO) based Gaussian process (GP) regression [16] have also been used since these do not require any training data unlike the supervised learning approaches. Recent demonstrations for small-angle x-ray scattering [17], scanning probe microscopy [18] and neutron triple axis spectrometry [19] further highlight the potential of such methods.…”