Zhang Chen, Niemann Valerie A, Benedek Peter, Jaramillo Thomas F, Doucet Mathieu
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States.
ACS Phys Chem Au. 2024 Nov 2;5(1):30-37. doi: 10.1021/acsphyschemau.4c00054. eCollection 2025 Jan 22.
Neutron-Transformer Reflectometry Advanced Computation Engine (), a neural network model using a 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 accelerate traditional approaches to modeling reflectometry data.
中子-变压器反射测量高级计算引擎（Neutron-Transformer Reflectometry Advanced Computation Engine），一种使用变压器架构的神经网络模型，被引入用于中子反射测量数据分析。它提供快速、准确的初始参数估计和高效的优化，提高了锂介导的电化学氨合成氮还原实时数据分析的效率和精度，与其他化学转化和电池相关。尽管在跨系统泛化方面存在局限性，但它显示出将变压器用作模型基础的潜力，这些模型可以加速传统的反射测量数据建模方法。
