The essential physics of dust grains are typically rationalised using phenomenological approaches, often assuming highly simplified grain morphologies. Such descriptions are necessary if the underlying microscopic details are unknown or too complex to model. For small nanosized dust particles, these constraints can be overcome by atomistic simulations that can provide realistic detailed grain models. We show how atomistic forcefield-based simulations can be harnessed to: i) model the growth of structurally realistic nanograins, and ii) calculate a range of astrophysically relevant physicochemical properties directly from the growing grains. We report the Nucleated Atomistic Grain Growth Simulator (NAGGS) as a new tool to model the growth of realistic nanosized dust grains through the progressive accretion of monomers onto a nucleated seed. NAGGS can be used with open source molecular dynamics codes, allowing for the modelling of grains that have different chemical compositions and are grown under a range of astrophysical conditions. To demonstrate how NAGGS works, we use it to produce 40 nanosilicate grain models with diameters of ∼3.5 nm and consisting of ∼1500 atoms. We consider Mg-rich olivinic and pyroxenic grains, and growth under two circumstellar dust-producing conditions. We calculate properties from the atomistically detailed nanograin structures (e.g. morphology, surface area, density, dipole moments) with respect to the size, chemical composition, and growth temperature of the grains. Our simulations reveal detailed new insights into the complex interacting degrees of freedom during grain growth and how they affect the resultant physicochemical properties. For example, we find that surface roughness depends on the Mg:Si ratio during growth. We also find that nanosilicates have very high dipole moments, which depend on the growth temperature. Such findings could have important consequences (e.g. astrochemistry, microwave emission). In summary, our bottom-up physically motivated approach offers a detailed understanding of nanograins that could help in both interpreting observations and improving dust models.
Guiu et al. (Tue,) studied this question.