The goal of specular neutron and x-ray reflectometry is to infer a material’s scattering length density (SLD) profile from its experimental reflectivity curves. This paper focuses on the investigation of an original approach to the ill-posed non-invertible problem which involves the use of artificial neural networks (ANNs). In particular, the numerical experiments described here deal with large data sets of simulated reflectivity curves and SLD profiles, and aim to assess the applicability of data science and machine learning technology to the analysis of data generated at large-scale neutron scattering facilities. It is demonstrated that, under certain circumstances, properly trained deep neural networks are capable of correctly recovering plausible SLD profiles when presented with previously unseen simulated reflectivity curves. When the necessary conditions are met, a proper implementation of the described approach would offer two main advantages over traditional fitting methods when dealing with real experiments, namely (1) sample physical models are described under a new paradigm: detailed layer-by-layer descriptions (SLDs, thicknesses, roughnesses) are replaced by parameter-free curves ρ(z), allowing a priori assumptions to be used in terms of the sample family to which a given sample belongs (e.g. ‘thin film,’ ‘lamellar structure’,etc.); (2) the time required to reach a solution is shrunk by orders of magnitude, enabling faster batch analysis for large datasets.

镜面中子和 X 射线反射计的目标是从材料的实验反射率曲线推断材料的散射长度密度 (SLD) 分布。本文重点研究涉及使用人工神经网络 (ANN) 的不适定不可逆问题的原始方法。特别是，这里描述的数值实验处理模拟反射率曲线和 SLD 剖面的大型数据集，旨在评估数据科学和机器学习技术对分析大型中子散射设施产生的数据的适用性。事实证明，在某些情况下，经过适当训练的深度神经网络能够正确地恢复合理的 SLD 轮廓，当呈现出以前看不见的模拟反射率曲线时。ρ ( z )，允许根据给定样本所属的样本族（例如“薄膜”、“层状结构”等）使用先验假设；(2) 达到解决方案所需的时间缩短了数量级，从而可以更快地对大型数据集进行批量分析。