Neutron-Transformer Reflectometry and Advanced Computation Engine (N-TRACE ),
a neural network model using transformer architecture, is introduced for
neutron reflectometry data analysis. It offers fast, accurate initial parameter
estimations and efficient refinements, improving efficiency and precision for
real-time data analysis of lithium-mediated nitrogen reduction for
electrochemical ammonia synthesis, with relevance to other chemical
transformations and batteries. Despite limitations in generalizing across
systems, it shows promises for the use of transformers as the basis for models
that could replace trial-and-error approaches to modeling reflectometry data.

引入了一种名为 N-TRACE（Neutron-Transformer Reflectometry and Advanced Computation Engine）的神经网络模型，使用 transformer 架构进行中子反射测量数据分析，提供快速、准确的初始参数估计和高效的细化，提高锂介导的氮还原电化学合成中实时数据分析的效率和精度，与其他化学转化和电池相关。尽管在系统间推广方面存在局限性，但它表现出 transformer 作为基于模型的反射测量数据建模的试错方法替代的潜力。