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In silico approaches for polymeric nanocomposites (Chapter 14)
Authors: Cova, T.; Nunes, S.; Vitorino, C.; Ferreira, M.; Rondon-Villarreal, P.; Pais, A.
Ref.: In-Silico Approaches to Macromolecular Chemistry (Elsevier) (2023)
Abstract: Polymer composites and nanocomposites have a wide range of applications in materials science and biomedicine, where both structural and functional requirements must be met. Conventional design approaches to synthesize and optimize these materials are mostly experimentally oriented. One step forward is to link experiments through a concrete design scheme to address key processes and validation of results. Recent advances in computational modeling and simulation driven by quantum computing and artificial intelligence have led to changes in the rational design of such versatile materials, enabling a deeper understanding of material formulation and behavior. Along the remarkable progress in computational materials engineering, the development of hybrid and multiscale approaches supported by machine learning algorithms is clearly a fruitful area of research. These approaches possess invaluable potential for exploring and predicting the behavior of polymer nanocomposites, discovering optimal materials and processes, and optimizing existing ones. This chapter aims to provide an overview of the computational approaches that support the experimental design of a variety of polymer nanocomposites and promote their performance evaluation in the pharmaceutical and biomedical fields. The focus is on the models, algorithms and methods proposed to facilitate the exploration of polymer nanocomposite design and synthesis and the prediction of soft material behavior. These will greatly expand the portfolio of in silico methods applied to these types of materials.
