Neutron reflectometry (NR) is a powerful tool for probing thin films at length scales down to nanometers. We investigated the use of a neural network to predict a two-layer thin film structure to model a given measured reflectivity curve. Application of this neural network to predict a thin film structure revealed that it was accurate and could provide an excellent starting point for traditional fitting methods. Employing prediction-guided fitting has considerable potential for more rapidly producing a result compared to the labor-intensive but commonly-used approach of trial and error searches prior to refinement. A deeper look at the stability of the predictive power of the neural network against statistical fluctuations of measured reflectivity profiles showed that the predictions are stable. We conclude that the approach presented here can provide valuable assistance to users of NR and should be further extended for use in studies of more complex n-layer thin film systems. This result also opens up the possibility of developing adaptive measurement systems in the future.

中子反射计 (NR) 是一种强大的工具，可用于探测纳米级长度的薄膜。我们研究了使用神经网络来预测两层薄膜结构以模拟给定的测量反射率曲线。将该神经网络应用于预测薄膜结构表明它是准确的，并且可以为传统的拟合方法提供一个很好的起点。与劳动密集型但常用的细化前反复试验搜索方法相比，采用预测引导拟合具有相当大的潜力，可以更快地产生结果。更深入地研究神经网络对测量反射率剖面的统计波动的预测能力的稳定性表明预测是稳定的。我们得出结论，这里介绍的方法可以为 NR 用户提供有价值的帮助，应该进一步扩展用于更复杂的 n 层薄膜系统的研究。这一结果也开辟了未来开发自适应测量系统的可能性。