Extracting thin film structures of energy materials using transformers

TL;DR

This paper introduces N-TRACE, a transformer-based neural network for neutron reflectometry data analysis, enabling faster and more accurate initial parameter estimation and refinement, with potential to replace trial-and-error methods.

Contribution

It presents a novel transformer-based neural network model specifically designed for neutron reflectometry data analysis, improving efficiency and accuracy.

Findings

N-TRACE provides fast initial parameter estimates.

It improves the precision of data analysis in real-time.

Shows potential to replace traditional trial-and-error methods.

Abstract

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.