Molecular simulation is applied to understanding the behaviour of alkane liquids with the eventual goal of being able to predict the viscosity of an arbitrary alkane mixture from first principles. Such prediction would have numerous scientific and industrial applications, as alkanes are the largest component of fuels, lubricants, and waxes; furthermore, they form the backbones of a myriad of organic compounds. This dissertation details the creation of a potential, a model for how the atoms and molecules in the simulation interact, based on a systematic approximation of the quantum mechanical potential energy surface using machine learning. This approximation has the advantage of producing forces and energies of nearly quantum mechanical accuracy at a tiny fraction of the usual cost. It enables accurate simulation of the large systems and long timescales required for accurate prediction of properties such as the density and viscosity. The approach is developed and tested on methane, the simplest alkane, and investigations are made into potentials for longer, more complex alkanes. The results show that the approach is promising and should be pursued further to create an accurate machine learning potential for the alkanes. It could even be extended to more complex molecular liquids in the future.

The ability to isolate single atomic layers of van der Waals materials has led to renewed interest in the electronic and optical properties of these materials as they can be fundamentally different at the monolayer limit. Moreover, these 2D crystals can be assembled together layer by layer, with controllable sequence and orientation, to form artificial materials that exhibit new features that are not found in monolayers nor bulk. Twisted bilayer graphene is one such prototype system formed by two monolayer graphene layers placed on top of each other with a twist angle between their lattices, whose electronic band structure depends on the twist angle. This thesis presents the efforts to explore the electronic and optical properties of twisted bilayer graphene by Raman spectroscopy and scanning tunneling microscopy measurements. We first synthesize twisted bilayer graphene with various twist angles via chemical vapor deposition. Using a combination of scanning tunneling microscopy and Raman spectroscopy, the twist angles are determined. The strength of the Raman G peak is sensitive to the electronic band structure of twisted bilayer graphene and therefore we use this peak to monitor changes upon doping. Our results demonstrate the ability to modify the electronic and optical properties of twisted bilayer graphene with doping. We also fabricate twisted bilayer graphene by controllable stacking of two graphene monolayers with a dry transfer technique. For twist angles smaller than one degree, many body interactions play an important role. It requires eight electrons per moire unit cell to fill up each band instead of four electrons in the case of a larger twist angle. For twist angles smaller than 0.4 degree, a network of domain walls separating AB and BA stacking regions forms, which are predicted to host topologically protected helical states. Using scanning tunneling microscopy and spectroscopy, these states are confirmed to appear on the domain walls when inversion symmetry is broken with an external electric field. We observe a double-line profile of these states on the domain walls, only occurring when the AB and BA regions are gaped. These states give rise to channels that could transport charge in a dissipationless manner making twisted bilayer graphene a promising platform to realize controllable topological networks for future applications.

Polymer Nanocomposites (PNC) are a new class of materials characterized by their large interfacial areas between the host polymer and nanofiller. This unique feature, due to the size of the nanofiller, is understood to be the cause of enhanced mechanical, electrical, optical, and barrier properties observed of PNCs, relative to the properties of the unfilled polymer. This interface can determine the miscibility of the nanofiller in the polymer, which, in turn, influences the PNC's properties. In addition, this interface alters the polymer's structure near the surface of the nanofiller resulting in heterogeneity of local properties that can be expressed at the macroscopic level. Considering the polymer-nanoparticle interface significantly influences PNC properties, it is apparent that some atomistic level of detail is required to accurately predict the behavior of PNCs. Though an all-atom simulation of a PNC would be able to accomplish the latter, it is an impractical approach to pursue even with the most advanced computational resources currently available. In this contribution, we develop (1) an equilibrium coarse-graining method to predict nanoparticle dispersion in a polymer melt, (2) a dynamic coarse-graining method to predict rheological properties of polymer-nanoparticle melt mixtures, and (3) a numerical approach that includes interfacial layer effects and polymer rigidity when predicting barrier properties of PNCs. In addition to the above, we study how particle and polymer characteristics affect the interfacial layer thickness as well as how the polymer-nanoparticle interface may influence the entanglement network in a polymer melt. More specifically, we use a mean-field theory approach to discern how the concentration of a semiflexible polymer, its rigidity and the particle's size determine the interfacial layer thickness, and the scaling laws to describe this dependency. We also utilize molecular dynamics and simulation techniques on a model PNC to determine if the polymer-nanoparticle interaction can influence the entanglement network of a polymer melt. / text