Hoogerheide David P, Heinrich Frank
NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.
Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
J Appl Crystallogr. 2024;57(4). doi: 10.1107/s1600576724006447.
Neutron reflectometry (NR) is a powerful technique for interrogating the structure of thin films at interfaces. Because NR measurements are slow and instrument availability is limited, measurement efficiency is paramount. One approach to improving measurement efficiency is active learning (AL), in which the next measurement configurations are selected on the basis of information gained from the partial data collected so far. , a model-based AL algorithm for neutron reflectometry measurements, is presented in this manuscript. uses the existing measurements of a function to choose both the position and the duration of the next measurement. maximizes the information acquisition rate in specific model parameters of interest and uses the well defined signal-to-noise ratio in counting measurements to choose appropriate measurement times. Since continuous measurement is desirable for practical implementation, features forecasting, in which the optimal positions of multiple future measurements are predicted from existing measurements. The performance of is compared with that of well established best practice measurements for supported lipid bilayer samples using realistic digital twins of monochromatic and polychromatic reflectometers. is shown to improve NR measurement speeds in all cases significantly.
中子反射测量技术（NR）是一种用于研究薄膜在界面处结构的强大技术。由于NR测量速度慢且仪器可用性有限，测量效率至关重要。提高测量效率的一种方法是主动学习（AL），即根据从目前收集的部分数据中获得的信息来选择下一个测量配置。本文提出了一种基于模型的用于中子反射测量的主动学习算法。该算法利用函数的现有测量值来选择下一次测量的位置和持续时间。它在感兴趣的特定模型参数中最大化信息获取率，并利用计数测量中定义明确的信噪比来选择合适的测量时间。由于实际应用中需要连续测量，该算法具有预测功能，即从现有测量值预测多个未来测量的最佳位置。使用单色和多色反射仪的逼真数字模型，将该算法的性能与已确立的支持脂质双层样品的最佳实践测量的性能进行了比较。结果表明，该算法在所有情况下都能显著提高NR测量速度。
