(162c) Thermodynamic and Data-Centric Computational Methods to Guide Nanoparticle Design
Here, we present computational approaches from our group that can guide the design of NPs for biomedical applications. We will first discuss the use of atomistic molecular dynamics simulations to gain physical insight into the effect of specific components of the NP design space on NP surface properties. In particular, we show that ligand fluctuations promoted by the length of the ligand backbone or the NP core size result in spatial variations in surface properties, even for uniform monolayer coatings, and illustrate how these spatial variations influence the magnitude of water-mediated interactions. We then discuss how the specific chemical properties of ligand end groups influence these fluctuations and resulting interactions. To complement these thermodynamic studies, we further apply deep learning to rapidly screen NP surface properties as a function of monolayer composition using minimal simulation input. In conjunction, these thermodynamic and data-centric techniques provide new computational tools that can be leveraged to tune NP surface properties and provide fundamental insight into behavior at nanomaterial interfaces.