A combination of graphene-like electrodes and ionic liquid (IL) electrolytes has emerged as a viable and attractive choice for electrochemical double layer (EDL) capacitors. Based on combined classical molecular dynamics and density functional theory calculations, we present the interfacial capacitance between planar graphene and [BMIM][PF 6 ] IL, with particular attention to the relative contributions of the electric double layer capacitance at the graphene/IL interface and the quantum capacitance of graphene. The microstructure of [BMIM][PF 6 ] near the graphene electrode with varying charge densities are investigated to provide a molecular description of EDLs, including BMIM/PF 6 packing and orientation, cation-anion segregation, and electrode charge screening. Although the IL interfacial structures exhibit an alternative cation/anion layering extending a few nanometers, the calculated potential profiles provide evidence of one-ion thick compact EDL formation. The capacitance-potential curve of the EDL is convex-or bell-shaped, whereas the quantum capacitance of graphene is found to have concave-or U-shaped characteristics with a minimum of nearly zero. Consequently, we find that the total interfacial capacitance exhibits a U-shaped trend, consistent with existing experimental observations at a typical carbon/IL interface. Our work highlights the importance of the quantum capacitance in the overall performance of graphene-based EDL capacitors.

Graphene-based electrodes have been widely tested and used in electrochemical double layer capacitors due to their high surface area and electrical conductivity. Nitrogen doping of graphene has recently been demonstrated to significantly enhance capacitance, but the underlying mechanisms remain ambiguous. We present the doping effect on the interfacial capacitance between graphene and [BMIM][PF 6 ] ionic liquid, particularly the relative changes in the double layer and electrode (quantum) capacitances. The electrode capacitance change was evaluated based on density functional theory calculations of doping-induced electronic structure modifications in graphene, while the microstructure and capacitance of the double layers forming near undoped/ doped graphene electrodes were calculated using classical molecular dynamics. Our computational study clearly demonstrates that nitrogen doping can lead to significant enhancement in the electrode capacitance as a result of electronic structure modifications while there is virtually no change in the double layer capacitance. This finding sheds some insight into the impact of the chemical and/or mechanical modifications of graphene-like electrodes on supercapacitor performance.

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations, however, are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and on-going development of a largely “bottom-up” coarse-grained (CG) model of the SARS-CoV-2 virion. Structural data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data becomes publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.