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How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. Machine learning methods are useful to infer QSPR (Quantitative Structure-Property Relationships) models that allow predicting mechanical properties related to the industrial profile of polymeric materials based on their structural repeating units (SRUs). Nonetheless, the chemical structure of the SRU is only one of the many factors that affects the industrial usefulness of a polymer. Other equally relevant factors are polymer molecular weight, molecular weight distribution, and production method, which are related to the inherent polydispersity of this kind of material. For this reason, the computational characterization used for the building of QSPR models for predicting mechanical properties should consider these main factors. The aim of this paper is to highlight recent advances in data science to address the inclusion of polydispersity information of polymeric materials in QSPR modeling. We present two dimensions of discussion: data representation and algorithmic issues. In the first one, we examine how different strategies can be applied to include polydispersity data in the molecular descriptors that characterize the polymers. We explain two data representation approaches designed by our group, named as trivalued and multivalued molecular descriptors. In the second dimension, we discuss algorithms proposed to deal with these new molecular descriptor representations during the construction of the QSPR models. Thus, we present here a comprehensible and integral methodology to address the challenges that polydispersity generates in the QSPR modeling of mechanical properties of polymers.
How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
Polymer informatics is an emerging discipline that has benefited from the strong development that data science has experienced over the last decade. Machine learning methods are useful to infer QSPR (Quantitative Structure-Property Relationships) models that allow predicting mechanical properties related to the industrial profile of polymeric materials based on their structural repeating units (SRUs). Nonetheless, the chemical structure of the SRU is only one of the many factors that affects the industrial usefulness of a polymer. Other equally relevant factors are polymer molecular weight, molecular weight distribution, and production method, which are related to the inherent polydispersity of this kind of material. For this reason, the computational characterization used for the building of QSPR models for predicting mechanical properties should consider these main factors. The aim of this paper is to highlight recent advances in data science to address the inclusion of polydispersity information of polymeric materials in QSPR modeling. We present two dimensions of discussion: data representation and algorithmic issues. In the first one, we examine how different strategies can be applied to include polydispersity data in the molecular descriptors that characterize the polymers. We explain two data representation approaches designed by our group, named as trivalued and multivalued molecular descriptors. In the second dimension, we discuss algorithms proposed to deal with these new molecular descriptor representations during the construction of the QSPR models. Thus, we present here a comprehensible and integral methodology to address the challenges that polydispersity generates in the QSPR modeling of mechanical properties of polymers.
How can polydispersity information be integrated in the QSPR modeling of mechanical properties?
F. Cravero (Autor:in) / S. A. Schustik (Autor:in) / M. J. Martínez (Autor:in) / M. F. Díaz (Autor:in) / I. Ponzoni (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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