Type Label Framework for Bonded Force Fields in LAMMPS.

New functionality is added to the LAMMPS molecular simulation package, which increases the versatility with which LAMMPS can interface with supporting software and manipulate information associated with bonded force fields. We introduce the "type label" framework that allows atom types and their higher-order interactions (bonds, angles, dihedrals, and impropers) to be represented in terms of the standard atom type strings of a bonded force field. Type labels increase the human readability of input files, enable bonded force fields to be supported by the OpenKIM repository, simplify the creation of reaction templates for the REACTER protocol, and increase compatibility with external visualization tools, such as VMD and OVITO. An introductory primer on the forms and use of bonded force fields is provided to motivate this new functionality and serve as an entry point for LAMMPS and OpenKIM users unfamiliar with bonded force fields. The type label framework has the potential to streamline modeling workflows that use LAMMPS by increasing the portability of software, files, and scripts for preprocessing, running, and postprocessing a molecular simulation.

CHARMM-GUI Nanomaterial Modeler for Modeling and Simulation of Nanomaterial Systems.

Molecular modeling and simulation are invaluable tools for nanoscience that predict mechanical, physicochemical, and thermodynamic properties of nanomaterials and provide molecular-level insight into underlying mechanisms. However, building nanomaterial-containing systems remains challenging due to the lack of reliable and integrated cyberinfrastructures. Here we present Nanomaterial Modeler in CHARMM-GUI, a web-based cyberinfrastructure that provides an automated process to generate various nanomaterial models, associated topologies, and configuration files to perform state-of-the-art molecular dynamics simulations using most simulation packages. The nanomaterial models are based on the interface force field, one of the most reliable force fields (FFs). The transferability of nanomaterial models among the simulation programs was assessed by single-point energy calculations, which yielded 0.01% relative absolute energy differences for various surface models and equilibrium nanoparticle shapes. Three widely used Lennard-Jones (LJ) cutoff methods are employed to evaluate the compatibility of nanomaterial models with respect to conventional biomolecular FFs: simple truncation at r = 12 Å (12 cutoff), force-based switching over 10 to 12 Å (10-12 fsw), and LJ particle mesh Ewald with no cutoff (LJPME). The FF parameters with these LJ cutoff methods are extensively validated by reproducing structural, interfacial, and mechanical properties. We find that the computed density and surface energies are in good agreement with reported experimental results, although the simulation results increase in the following order: 10-12 fsw <12 cutoff < LJPME. Nanomaterials in which LJ interactions are a major component show relatively higher deviations (up to 4% in density and 8% in surface energy differences) compared with the experiment. Nanomaterial Modeler's capability is also demonstrated by generating complex systems of nanomaterial-biomolecule and nanomaterial-polymer interfaces with a combination of existing CHARMM-GUI modules. We hope that Nanomaterial Modeler can be used to carry out innovative nanomaterial modeling and simulations to acquire insight into the structure, dynamics, and underlying mechanisms of complex nanomaterial-containing systems.

Molecular dynamics simulations of nano-confined methanol and methanol-water mixtures between infinite graphite plates: Structure and dynamics.

Molecular dynamics simulations are used to investigate microscopic structures and dynamics of methanol and methanol-water binary mixture films confined between hydrophobic infinite parallel graphite plate slits with widths, H, in the range of 7-20 Å at 300 K. The initial geometric densities of the liquids were chosen to be the same as bulk methanol at the same temperature. For the two narrowest slit widths, two smaller initial densities were also considered. For the nano-confined system with H = 7 Å and high pressure, a solid-like hexagonal arrangement of methanol molecules arranged perpendicular to the plates is observed which reflects the closest packing of the molecules and partially mirrors the structure of the underlying graphite structure. At lower pressures and for larger slit widths, in the contact layer, the methanol molecules prefer having the C-O bond oriented parallel to the walls. Layered structures of methanol parallel to the wall were observed, with contact layers and additional numbers of central layers depending on the particular slit width. For methanol-water mixtures, simulations of solutions with different composition were performed between infinite graphite slits with H = 10 and 20 Å at 300 K. For the nanoslit with H = 10 Å, in the solution mixtures, three layers of molecules form, but for all mole fractions of methanol, methanol molecules are excluded from the central fluid layer. In the nanopore with H = 20 Å, more than three fluid layers are formed and methanol concentrations are enhanced near the confining plates walls compared to the average solution stoichiometry. The self-diffusion coefficients of methanol and water molecules in the solution show strong dependence on the solution concentration. The solution mole fractions with minimal diffusivity are the same in confined and non-confined bulk methanol-water mixtures.

A Parallel Distributed-Memory Particle Method Enables Acquisition-Rate Segmentation of Large Fluorescence Microscopy Images.

Modern fluorescence microscopy modalities, such as light-sheet microscopy, are capable of acquiring large three-dimensional images at high data rate. This creates a bottleneck in computational processing and analysis of the acquired images, as the rate of acquisition outpaces the speed of processing. Moreover, images can be so large that they do not fit the main memory of a single computer. We address both issues by developing a distributed parallel algorithm for segmentation of large fluorescence microscopy images. The method is based on the versatile Discrete Region Competition algorithm, which has previously proven useful in microscopy image segmentation. The present distributed implementation decomposes the input image into smaller sub-images that are distributed across multiple computers. Using network communication, the computers orchestrate the collectively solving of the global segmentation problem. This not only enables segmentation of large images (we test images of up to 10(10) pixels), but also accelerates segmentation to match the time scale of image acquisition. Such acquisition-rate image segmentation is a prerequisite for the smart microscopes of the future and enables online data compression and interactive experiments.