Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials

Autor: Goele Magchiels, Niels Claessens, Johan Meersschaut, André Vantomme
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-024-58265-7
Popis: Abstract We address the high accuracy and precision demands for analyzing large in situ or in operando spectral data sets. A dual-input artificial neural network (ANN) algorithm enables the compositional and depth-sensitive analysis of multinary materials by simultaneously evaluating spectra collected under multiple experimental conditions. To validate the developed algorithm, a case study was conducted analyzing complex Rutherford backscattering spectrometry (RBS) spectra collected in two scattering geometries. The dual-input ANN analysis excelled in providing a systematic analysis and precise results, showcasing its robustness in handling complex data and minimizing user bias. A comprehensive comparison with human supervision analysis and conventional single-input ANN analysis revealed a reduced susceptibility of the dual-input ANN analysis to inaccurately known setup parameters, a common challenge in material characterization. The developed multi-input approach can be extended to a wide range of analytical techniques, in which the combined analysis of measurements performed under different experimental conditions is beneficial for disentangling details of the material properties.
Databáze: Directory of Open Access Journals
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