Prediction of coating thickness for polyelectrolyte multilayers via machine learning

Abstract Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final propert...

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Autores principales:	Varvara Gribova, Anastasiia Navalikhina, Oleksandr Lysenko, Cynthia Calligaro, Eloïse Lebaudy, Lucie Deiber, Bernard Senger, Philippe Lavalle, Nihal Engin Vrana
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7f17c1a234dc4d0588af8daa1200d5f5
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spelling	oai:doaj.org-article:7f17c1a234dc4d0588af8daa1200d5f52021-12-02T18:48:09ZPrediction of coating thickness for polyelectrolyte multilayers via machine learning10.1038/s41598-021-98170-x2045-2322https://doaj.org/article/7f17c1a234dc4d0588af8daa1200d5f52021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98170-xhttps://doaj.org/toc/2045-2322Abstract Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we first analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufficient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the first time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast first response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-inflammatory properties.Varvara GribovaAnastasiia NavalikhinaOleksandr LysenkoCynthia CalligaroEloïse LebaudyLucie DeiberBernard SengerPhilippe LavalleNihal Engin VranaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Varvara Gribova Anastasiia Navalikhina Oleksandr Lysenko Cynthia Calligaro Eloïse Lebaudy Lucie Deiber Bernard Senger Philippe Lavalle Nihal Engin Vrana Prediction of coating thickness for polyelectrolyte multilayers via machine learning
description	Abstract Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we first analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufficient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the first time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast first response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-inflammatory properties.
format	article
author	Varvara Gribova Anastasiia Navalikhina Oleksandr Lysenko Cynthia Calligaro Eloïse Lebaudy Lucie Deiber Bernard Senger Philippe Lavalle Nihal Engin Vrana
author_facet	Varvara Gribova Anastasiia Navalikhina Oleksandr Lysenko Cynthia Calligaro Eloïse Lebaudy Lucie Deiber Bernard Senger Philippe Lavalle Nihal Engin Vrana
author_sort	Varvara Gribova
title	Prediction of coating thickness for polyelectrolyte multilayers via machine learning
title_short	Prediction of coating thickness for polyelectrolyte multilayers via machine learning
title_full	Prediction of coating thickness for polyelectrolyte multilayers via machine learning
title_fullStr	Prediction of coating thickness for polyelectrolyte multilayers via machine learning
title_full_unstemmed	Prediction of coating thickness for polyelectrolyte multilayers via machine learning
title_sort	prediction of coating thickness for polyelectrolyte multilayers via machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7f17c1a234dc4d0588af8daa1200d5f5
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Prediction of coating thickness for polyelectrolyte multilayers via machine learning

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