Prediction of coating thickness for polyelectrolyte multilayers via machine learning.
Fiche publication
Date publication
septembre 2021
Journal
Scientific reports
Auteurs
Membres identifiés du Cancéropôle Est :
Dr LAVALLE Philippe
Tous les auteurs :
Gribova V, Navalikhina A, Lysenko O, Calligaro C, Lebaudy E, Deiber L, Senger B, Lavalle P, Vrana NE
Lien Pubmed
Résumé
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.
Référence
Sci Rep. 2021 Sep 21;11(1):18702