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    Area of Science:

    • Materials Science
    • Computational Chemistry
    • Nanotechnology

    Background:

    • Simulating nanoparticle systems computationally often involves a trade-off between model detail and system size.
    • Existing models struggle to balance accuracy with the ability to simulate a large number of nanoparticles.

    Purpose of the Study:

    • To develop a coarse-grained model for amorphous silica nanoparticles.
    • To enable large-scale simulations of realistic nanoparticle systems.
    • To provide a transferable and extensible modeling approach.

    Main Methods:

    • Developed a coarse-grained model for amorphous silica nanoparticles.
    • Derived model parameters by matching to atomistic nanoparticle simulation data.
    • Optimized interaction parameters for a range of nanoparticle sizes.
    • Determined analytical functions for parameter acquisition at arbitrary coarse-grained resolutions.

    Main Results:

    • Successfully created a coarse-grained model for amorphous silica nanoparticles.
    • Achieved accurate representation of nanoparticles across various diameters.
    • Demonstrated the model's extensibility to cross-interactions and grafted nanoparticle systems.
    • The parameter optimization procedure is available as an open-source Python package.

    Conclusions:

    • The developed coarse-grained model enables large-scale simulations of realistic amorphous silica nanoparticle systems.
    • The parameter derivation and optimization method is transferable and extensible to other nanoparticle types.
    • This work facilitates more comprehensive computational studies of complex nanoparticle assemblies.