![]() Additionally, some Quantitative Structure-Property Relationship models for predicting RI, inferred without domain expert intervention, are presented and discussed for illustrating how virtual testing can be applied using this database. This paper details the different types of errors found in the data source and the corrections made during the curation and cleaning of this database. In this work, we have focused on the generation of a trustworthy database of Refractive Index (RI) of synthetic polymers. Therefore, it is especially important to create reliable databases for polymer study and make them available to the scientific community. Nevertheless, the lack of data for learning virtual testing models constitutes a hard challenge for progressing in these innovative techniques. In this sense, the production of new materials could take advantage of novel virtual testing approaches based on data science for supporting the design of new polymers. The aim of industry 4.0 is to promote productivity and innovation by incorporating emerging IT technologies, where machine learning is playing a central role in this industrial revolution.
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