Simulated nanoparticle assembly using protoparticles (SNAP)

Abstract

Many functional properties of particle system rely on collective behaviour and the type of superstructures formed when thousands of particles come together. Self-assembly, agglomeration and aggregation depend sensitively on the size and shape of particles present, as well as the size distribution and the mixtures of shapes within a given sample, which makes simulation of these superstructures and their properties challenging. Here we present a new, flexible, software package for the simulations of ordered and disorder aggregates of faceted polyhedral particle from the nanoscale to the micronscale, which is capable of including size distributions and mixtures of multiple particle shapes defined by the User, subject to additional User-defined interactions. Following relaxation using molecular dynamics a number of characterisation tools are provided, including interfacial probabilities and distribution functions. The software is applicable to a range of problems from nanoparticle assembly to additive manufacturing.Aggregates of nanoparticles can provide a range of unique properties that are different from isolated, individual entities. Depending on the particle size(s) and shape(s), the overall porosity, strength and complexity of the aggregates the reactivity, transport and efficiency of entire samples can be controlled [1][2][3][4][5][6][7]. Examples include the tribological properties of powders [8], the toxicological properties of colloids [9], the mechanical properties of composites [10], the collective electromagnetic properties exhibited in surface plasmon resonances and nanoporous materials formed via aggregation or self-assembly [11].Characterising the superstructure of aggregates is much more difficult than ordered self-assemblies of nanoparticles, due to the variations in particles sizes, shapes, interaction points, types of interactions and the inherent randomness. Probing with experimental methods can be challenging due to interference from the probe and the collection of either averaged results that represent entire samples, or local results (limited by the probe size) that may not be representative at all. Both local interactions and long-ranged statistics can be obtained from computational methods, but these have their challenges too. Simulations without periodic boundary conditions (PBCs) lack the ability to control the number density, while simulations with PBCs impose an unnatural translational symmetry and necessitates larger systems or repetition to develop the right statistics. The use of classical atomistic simulations methods limits the number of nanoparticles that can be practically handled, while the use of electronic structure methods limits the size and number of nanoparticles that can be handled, leading to an artificial bias toward minority features such as edges and corners. The aggregation of most realistic systems are driven by formation of either facet-facet interfaces (depending on the size and type of the facets) and the formation of voids. This cannot be achieved if we are limite...

Simulating Facet‐Dependent Aggregation and Assembly of Mixtures of Polyhedral Nanoparticles

“…SNAP is a graphical processing unit (GPU) enabled software package (serial and parallel) that is divided into three programs which represent the various stages of preparing, running, and analyzing a given simulation. [ 29,30 ] The Generator builds user‐defined nanoparticle sizes and shapes, prepares the surface mesh to describe the surface properties, and establishes the initial configuration of nanoparticles in the simulation cell. The Simulator takes in the initial configuration, potential parameters, and simulation conditions defined by the user to run MD.…”

“…A surface mesh density of 0.2 protoparticles Å −2 was used in this study to stop nanoparticle overlap and is governed by the range of the repulsive part of the Morse potential, as described in ref. [30]. This is an adjustable parameter.…”

“…Two interaction potentials are employed within the MD simulation, as described in detail in ref. [30]. The first is a harmonic potential between neighboring protoparticles on the same nanoparticle used to preserve nanoparticle shape.…”

“…These facet‐to‐facet parameters are then converted to point‐to‐point interaction parameters for the surface mesh of protoparticles for SNAP via a fitting Monte Carlo routine prior to the MD simulation, as described in ref. [30]. This is an automated conversion, so users only need to calculate the facet‐to‐facet parameters using their preferred higher‐order method.…”

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations

“…Simulation nanoparticle assembly using protoparticles (SNAP) was used to investigate the meso-structure of aggregated faceted polyhedral nanodiamonds. Details of the SNAP coarse-grained molecular dynamics approach have been published elsewhere . The package has been used previously to investigate nanodiamond self-assembly, ,, and we use the same polyhedral particle models and facet interactions here.…”