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.

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.

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 packaging and budding of Gag polyprotein and viral RNA is a critical step in the HIV-1 life cycle. High-resolution structures of the Gag polyprotein have revealed that the capsid (CA) and spacer peptide 1 (SP1) domains contain important interfaces for Gag self-assembly. However, the molecular details of the multimerization process, especially in the presence of RNA and the cell membrane, have remained unclear. In this work, we investigate the mechanisms that work in concert between the polyproteins, RNA, and membrane to promote immature lattice growth. We develop a coarse-grained (CG) computational model that is derived from subnanometer resolution structural data. Our simulations recapitulate contiguous and hexameric lattice assembly driven only by weak anisotropic attractions at the helical CA-SP1 junction. Importantly, analysis from CG and single-particle tracking photoactivated localization (spt-PALM) trajectories indicates that viral RNA and the membrane are critical constituents that actively promote Gag multimerization through scaffolding, while overexpression of short competitor RNA can suppress assembly. We also find that the CA amino-terminal domain imparts intrinsic curvature to the Gag lattice. As a consequence, immature lattice growth appears to be coupled to the dynamics of spontaneous membrane deformation. Our findings elucidate a simple network of interactions that regulate the early stages of HIV-1 assembly and budding.

Recent progress in coarse-grained (CG) molecular modeling and simulation has facilitated an influx of computational studies on biological macromolecules and their complexes. Given the large separation of length- and time-scales that dictate macromolecular biophysics, CG modeling and simulation are well-suited to bridge the microscopic and mesoscopic or macroscopic details observed from all-atom molecular simulations and experiments, respectively. In this review, we first summarize recent innovations in the development of CG models, which broadly include structure-based, knowledge-based, and dynamics-based approaches. We then discuss recent applications of different classes of CG models to explore various macromolecular complexes. Finally, we conclude with an outlook for the future in this ever-growing field of biomolecular modeling.

Coarse-grained (CG) models facilitate efficient simulation of complex systems by integrating out the atomic, or fine-grained (FG), degrees of freedom. Systematically-derived CG models from FG simulations often attempt to approximate the CG potential of mean force (PMF), an inherently multidimensional and many-body quantity, using additive pairwise contributions. However, they currently lack fundamental principles that enable their extensible use across different thermodynamic state points, i.e., transferability. In this work, we investigate the explicit energy-entropy decomposition of the CG PMF as a means to construct transferable CG models. In particular, despite its high-dimensional nature, we find for liquid systems that the entropic component to the CG PMF can similarly be represented using additive pairwise contributions, which we show is highly coupled to the CG configurational entropy. This approach formally connects the missing entropy that is lost due to the CG representation, i.e., translational, rotational and vibrational modes associated with the missing degrees of freedom, to the CG entropy. By design, the present framework imparts transferable CG interactions across different temperatures due to the explicit definition of an additive entropic contribution. Furthermore, we demonstrate that transferability across composition state points, such as between bulk liquids and their mixtures, is also achieved by designing combining rules to approximate cross-interactions from bulk CG PMFs. Using the predicted CG model for liquid mixtures, structural correlations of the fitted CG model were found to corroborate a high-fidelity combining rule. Our findings elucidate the physical nature and compact representation of CG entropy and suggest a new approach for overcoming the transferability problem. We expect this approach will further extend the current view of CG modeling into predictive multiscale modeling.

ABSTRACT:The dielectric constant or relative permittivity (r) of a dielectric material, which describes how the net electric field in the medium is reduced with respect to the external field, is a parameter of critical importance for charging and screening in electronic devices. Such a fundamental material property is intimately related to not only the polarizability of individual atoms, but also the specific atomic arrangement in the crystal lattice. In this letter, we present both experimental and theoretical investigations on the dielectric constant of few-layer In2Se3 nano-flakes grown on mica substrates by van der Waals epitaxy. A nondestructive microwave impedance microscope is employed to simultaneously quantify the number of layers and local electrical properties. The measured r increases monotonically as a function of the thickness and saturates to the bulk value at around 6 ~ 8 quintuple layers. The same trend of layer-dependent dielectric constant is also revealed by first-principle calculations. Our results of the dielectric response, being ubiquitously applicable to layered 2D semiconductors, are expected to be significant for this vibrant research field.KEYWORDS: microwave impedance microscopy, layer-dependent dielectric constant, In2Se3 nano-flakes, layered materials, polarization, first-principle calculations 2The rapid rise of graphene in the past decade has led to an active research field on twodimensional (2D) layered materials.1, 2 Of particular interest here are layered semiconductors, such as many metal chalcogenides, for their roles as gate dielectrics or channel materials in nextgeneration electronics. 3 Due to the strong intralayer covalent bonding and weak van der Waals (vdW) interactions, most physical properties are already anisotropic in the bulk form, with a clear 3D-2D crossover when approaching the monolayer thickness. In particular, the number of layers (n) in a thin-film 2D system is expected to strongly influence its dielectric constant, a fundamental electrical property that determines the capacitance and charge screening in electronic devices. 4-6The 2D material in this study is the layered semiconducting chalcogenide In2Se3, a technologically important system for phase-change memory, thermoelectric, and photoelectric applications 7 . The In-Se phase diagram is among the most complex ones in binary compounds.Even at the exact stoichiometry of In:Se = 2:3, multiple phases can occur under different temperatures and pressures. [8][9][10][11] By controlling the synthesis parameters or thermal/electrical pretreatment processes, several phases (superlattice, simple hexagonal -phase, simple hexagonal -phase, and amorphous state) with vastly different electrical conductivity can coexist at the ambient condition, 12-14 which explains the research interest of In2Se3 as a prototypical phasechange material. 7,[12][13][14][15][16][17][18] In addition, the lattice constant of In2Se3 matches well with Bi2Se3, which is heavily investigated for its high thermoelectric figure-of-merit and ...