467583 A Transferable Multi-Resolution Coarse-Grained Model for Amorphous Silica Nanoparticles

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Andrew Z. Summers, Olivia M. Cane, Christopher R. Iacovella, Peter T. Cummings and Clare McCabe, Chemical and Biomolecular Engineering, Vanderbilt University, Nashville, TN

Silica nanoparticles are ubiquitous in modern day nanoscience, with drug delivery, biomedical imaging, and polymer nanocomposites highlighting a host of pertinent applications. The balance between attractive and repulsive forces at the atomic level governs assembly of these materials into complex macroscale structures with a wide range of properties. Molecular dynamics simulations provide an avenue to examine nanoparticles with atomic-level resolution and to precisely tune the interactions between them. However, relevant nanoparticle sizes typically feature tens or hundreds of thousands of atoms, making simulations of more than a few nanoparticles computationally burdensome. Several coarse-grained (CG) models have been developed for amorphous silica nanoparticles in attempt to overcome this challenge, e.g., Ghanbari et al. constructed nanoparticles using CG particles representing individual SiO2 subunits [1] and Lee and Hua developed a model where CG beads represented spherical Si6O12 subunits [2]. However, neither of these models were compared directly to experimental or all-atom simulation data for amorphous silica nanoparticles and thus their efficacy and accuracy remain unproven.

In this work, we develop a CG model for amorphous silica nanoparticles using a potential-matching approach. Here, nanoparticles are represented as a collection of individual CG beads, modeled via a Mie potential, whose parameters are tuned such that the effective interaction of the collection of CG particles matches the interaction between atomistically detailed amorphous silica nanoparticles. To generate potentials that are transferable, CG parameters are derived to simultaneously match a range of nanoparticle sizes. Collective optimization of the parameters makes use of the constrained optimization by linear approximation (COBYLA) method provided by the openMDAO framework [3], resulting in CG potentials that match their atomistic counterparts with high fidelity. Higher and lower resolution models are also presented, corresponding to changes in the CG particle surface density, where such changes are made trivially using the mBuild software package [4]. Point particle models of NPs, representing the lowest resolution of coarse-graining, are also presented. We further demonstrate the extension of this potential matching procedure to obtain cross-interactions between CG particles and alkanes, allowing alkane-grafted CG amorphous silica nanoparticles to be constructed, whose properties show close agreement with atomistic models.

[1]  A. Ghanbari, T. V. M. Ndoro, F. Leroy, M. Rahimi, M. C. Böhm, and F. Müller-Plathe, “Interphase Structure in Silica–Polystyrene Nanocomposites: A Coarse-Grained Molecular Dynamics Study,” Macromolecules, vol. 45, no. 1, pp. 572–584, 2012.

[2]  C. K. Lee and C. C. Hua, “Nanoparticle interaction potentials constructed by multiscale computation,” J. Chem. Phys., vol. 132, no. 22, 2010.

[3] J. Gray, K. T. Moore, T. A. Hearn, and B. A. Naylor, “Standard Platform for Benchmarking Multidisciplinary Design Analysis and Optimization Architectures,” AIAA Journal, vol. 51, pp. 2380-2394, 2013.

[4] C. Klein, J. Sallai, T. J. Jones, C. R. Iacovella, C. McCabe, P. T. Cummings, "A hierarchical, component based approach to screening properties of soft matter," Accepted at Mol. Mod. and Sim.: Applications and Perspectives, 2016.

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