Accelerating the Materials-by-Design Approach for Hairy Nanoparticle Assemblies using Coarse-grained Molecular Dynamics Simulations

Hansoge, Nitin Krishnamurthy

doi:https://doi.org/10.21985/n2-d0w8-dh63

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Accelerating the Materials-by-Design Approach for Hairy Nanoparticle Assemblies using Coarse-grained Molecular Dynamics Simulations

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Recent advancements in processing and manufacturing techniques have spurred an exponential increase in use of polymer nanocomposites in a variety of applications. A key challenge in using these nanocomposites effectively is the dispersion of nanoparticles in the polymer matrix. Matrix-free assemblies of polymer grafted nanoparticles, called hairy nanoparticles assemblies(aHNPs) have come into spotlight as they overcome the dispersion issues of traditional polymer nanocomposites. These aHNPs provide better structural order that allows functional and mechanical properties to be tailored more accurately.Traditional material development involves iterative experimentations to characterize and optimize the mechanical properties, which is a costly and time-consuming process. The timeframe, from material discovery to market deployment, can be significantly reduced with the help of high performance computational tools. In this work, we describe computational approaches that significantly accelerate the materials-by-design process. The first step towards achieving this goal is to develop an effective coarse-graining (CG) strategy, namely the energy renormalization approach, that enhances the spatiotemporal scales of molecular dynamics (MD) simulations while quantitatively capturing the thermomechanical behavior of materials. Next, we apply these CG techniques to model the aHNPs and explore the mechanics of these materials. While experimental studies have characterized the mechanical properties of aHNPs, effective strategies to improve both the mechanical stiffness and toughness of aHNPs are lacking given the general conflicting nature of these two properties and the large number of molecular parameters involved in the design of aHNPs. Thus, we develop a computational framework combining machine learning with CG-MD simulations, called metamodel based design optimization, to establish design strategies for achieving optimal mechanical properties. We develop theoretical scaling laws that govern the polymer chain conformations in these aHNPs and show the universality of these laws by examining the conformations of grafted polymers with varying chemistry, persistence length and side-group size. Finally, we develop an effective interaction between nanoparticles in aHNPs with different design parameters, i.e., polymer chain length, grafting density and polymer chemistry using the potential of mean force approach. With the development of this interatomic potential between the nanoparticles, we propose a mesoscopic model for nanoparticle assemblies that circumvents the need to explicitly simulate polymer chains, significantly improving the computational efficiency by extending the spatiotemporal scales by 6-7 orders of magnitude. All-together, these studies provide guidance and strategies to accelerate the materials-by design approach for hairy nanoparticle assemblies. The insights obtained from this work will lay the foundation in advancing mechanical performance of composites and other relevant structural materials.

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