Abstract
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. 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. Nevertheless, the lack of data for learning virtual testing models constitutes a hard challenge for progressing in these innovative techniques. Therefore, it is especially important to create reliable databases for polymer study and make them available to the scientific community. In this work, we have focused on the generation of a trustworthy database of Refractive Index (RI) of synthetic polymers. 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. 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.
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References
Peerless, J.S., Milliken, N.J., Oweida, T.J., Manning, M.D., Yingling, Y.G.: Soft matter informatics: current progress and challenges. Adv. Theory Simul. 2(1), 1800129 (2019)
Xu, Q., Jiang, J.: Machine learning for polymer swelling in liquids. ACS Appl. Polym. Mater. 2(8), 3576–3586 (2020)
Audus, D.J., de Pablo, J.J.: Polymer informatics: opportunities and challenges. ACS Macro Lett. 6(10), 1078–1082 (2017)
Jha, A., Chandrasekaran, A., Kim, C., Ramprasad, R.: Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures. Model. Simul. Mater. Sci. Eng. 27(2), 024002 (2019)
de Pablo, J.J., et al.: New frontiers for the materials genome initiative. NPJ Comput. Mater. 5(1), 41 (2019)
Jabeen, F., Chen, M., Rasulev, B., Ossowski, M., Boudjouk, P.: Refractive indices of diverse data set of polymers: a computational QSPR based study. Comput. Mater. Sci. 137, 215–224 (2017)
Bicerano, J.: Prediction of Polymer Properties. CRC Press, Boca Raton (2002)
Duchowicz, P.R., Fioressi, S.E., Bacelo, D.E., Saavedra, L.M., Toropova, A.P., Toropov, A.A.: QSPR studies on refractive indices of structurally heterogeneous polymers. Chemom. Intell. Lab. Syst. 140, 86–91 (2015)
Xu, J., Chen, B., Zhang, Q., Guo, B.: Prediction of refractive indices of linear polymers by a four-descriptor QSPR model. Polymer 45(26), 8651–8659 (2004)
Khan, P.M., Rasulev, B., Roy, K.: QSPR modeling of the refractive index for diverse polymers using 2D descriptors. ACS Omega 3(10), 13374–13386 (2018)
Seferis, J.C. Refractive Indices of Polymers. The Wiley Database of Polymer Properties (2003)
Kim, S., et al.: PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 47(D1), D1102–D1109 (2019)
Molinspiration Cheminformatics: Nova ulica, SK-900 26 Slovensky Grob, Slovak Republic. https://www.molinspiration.com/cgi-bin/galaxy. Accessed 24 Aug 2020
Sigma-Aldrich Product Catalog: Polymer Science. https://www.sigmaaldrich.com/materials-science/polymer-science. Accessed 6 Aug 2020
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Roy, K., Das, R.N., Ambure, P., Aher, R.B.: Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr. Intell. Lab. Syst. 152, 18–33 (2016)
Muller, C., et al.: Prediction of drug induced liver injury using molecular and biological descriptors. Comb. Chem. High Throughput Screen. 18(3), 315–322 (2015)
DRAGON for Windows: (Software for Molecular Descriptor Calculations), Talete srl, Version 5.5. Milan, Italy (2007)
Topliss, J.G., Costello, R.J.: Chance correlations in structure-activity studies using multiple regression analysis. J. Med. Chem. 15(10), 1066–1068 (1972)
Martínez, M., Ponzoni, I., Díaz, Mónica. F., Vazquez, G., Soto, A.: Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods. J. Cheminform. 7(1), 1–17 (2015). https://doi.org/10.1186/s13321-015-0092-4
Acknowledgments
This work was partially supported by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET for its acronym in Spanish) [grant PIP 112–2017-0100829], by the Agencia Nacional de Promoción Científica y Tecnológica [grant PICT 2018–04533] and by the Universidad Nacional del Sur (UNS), Bahía Blanca, Argentina [grants PGI 24/N042 and PGI 24/ZM17].
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Schustik, S.A., Cravero, F., Ponzoni, I., Díaz, M.F. (2021). A Database Curation for Prediction of the Refractive Index in the Virtual Testing of Polymeric Materials by Using Machine Learning. In: Rossit, D.A., Tohmé, F., Mejía Delgadillo, G. (eds) Production Research. ICPR-Americas 2020. Communications in Computer and Information Science, vol 1408. Springer, Cham. https://doi.org/10.1007/978-3-030-76310-7_22
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DOI: https://doi.org/10.1007/978-3-030-76310-7_22
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