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Analysis of Dynamic EPR Spectra of pH-Sensitive Nitroxides Using Machine Learning

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Abstract

This research presents the application and comparison of the most popular machine learning algorithms (multilayer perceptron, ensemble of networks, convolutional neural network) to predict the shape of dynamic EPR signals of pH-sensitive nitroxide radicals (NR) with varying degrees of rotational correlation from EPR spectra obtained during the analysis of solid-phase systems. A training sample example consisting of theoretically modelled EPR spectra of pH-sensitive nitroxide radicals was prepared for the network learning. The result of applying and comparing machine learning algorithms with results obtained from simulations by the specialized software ODFR4 demonstrated that the multilayer perceptron and ensemble of networks have good accuracy in prediction signal shapes, however, within rather narrow scope of applicability. The convolutional neural network showed the worst results in predicting signal shapes. The results of this work can be useful for the development of software design to quickly estimate the parameters of analyzed systems by processing the EPR spectra of pH-sensitive nitroxide radicals.

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Acknowledgements

The study was supported by a grant in the form of subsidy No. 075-15-2022-1251 dated 12/13/2022 between Ural Federal University and the Ministry of Science and Higher Education of the Russian Federation.

Funding

The study was supported by a grant in the form of subsidy No. 075–15-2022–1251 dated 12/13/2022 between Ural Federal University and the Ministry of Science and Higher Education of the Russian Federation.

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Authors and Affiliations

  1. Institute of Chemical Technology, Ural Federal University, 620002, Ekaterinburg, Russia

    D. R. Davydov, D. O. Antonov & E. G. Kovaleva

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  1. D. R. Davydov
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  3. E. G. Kovaleva
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Contributions

DR Davydov was preparing the text of the manuscript and preparing the training set and developing neural network topologies. DO Antonov was preparing samples and obtaining electron paramagnetic resonance spectra. EG Kovaleva prepared the text and pictures of the manuscript. All authors reviewed the manuscript.

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Correspondence to D. R. Davydov.

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We give our consent for the publication of the following manuscript to be published in the Journal–Applied Magnetic Resonance. This consent statement was discussed with E. G. Kovaleva and D. O. Antonov, who are co-authors of this research article. We understand that this copy may be available in both print and in the Internet, and will be available to a broader audience through marketing channels and other third parties. Therefore, anyone can read material published in the Journal.

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Davydov, D.R., Antonov, D.O. & Kovaleva, E.G. Analysis of Dynamic EPR Spectra of pH-Sensitive Nitroxides Using Machine Learning. Appl Magn Reson 54, 595–612 (2023). https://doi.org/10.1007/s00723-023-01531-0

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