Extracting Thin Film Structures of Energy Materials Using Transformers.

IF 4.3 Q2 CHEMISTRY, PHYSICAL ACS Physical Chemistry Au Pub Date : 2024-11-02 eCollection Date: 2025-01-22 DOI:10.1021/acsphyschemau.4c00054
Chen Zhang, Valerie A Niemann, Peter Benedek, Thomas F Jaramillo, Mathieu Doucet
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Abstract

Neutron-Transformer Reflectometry Advanced Computation Engine (N-TRACE), 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.

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利用变压器提取能源材料薄膜结构。
中子-变压器反射高级计算引擎(N-TRACE)是一种基于变压器结构的神经网络模型,用于中子反射数据分析。它提供了快速、准确的初始参数估计和高效的改进,提高了电化学氨合成中锂介导的氮还原的实时数据分析的效率和精度,与其他化学转化和电池相关。尽管在跨系统推广方面存在局限性,但它显示了使用变压器作为模型基础的前景,可以加速传统方法对反射测量数据的建模。
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CiteScore
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期刊介绍: ACS Physical Chemistry Au is an open access journal which publishes original fundamental and applied research on all aspects of physical chemistry. The journal publishes new and original experimental computational and theoretical research of interest to physical chemists biophysical chemists chemical physicists physicists material scientists and engineers. An essential criterion for acceptance is that the manuscript provides new physical insight or develops new tools and methods of general interest. Some major topical areas include:Molecules Clusters and Aerosols; Biophysics Biomaterials Liquids and Soft Matter; Energy Materials and Catalysis
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