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Register a new userScaling-up Simulations of Diffusion in Microporous Materials
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We introduce and demonstrate the coarse-graining of static and dynamical properties of host-guest systems constituted by methane in two different microporous materials. The reference systems are mapped to occupancy-based pore-scale lattice models. Each coarse-grained model is equipped with an appropriate coarse-grained potential and a local dynamical operator, which represents the probability of inter-pore molecular jumps between different cages. Both the coarse-grained thermodynamics and dynamics are defined based on small-scale atomistic simulations of the reference systems. We considered two host materials: the widely-studied ITQ-29 zeolite and the LTA-zeolite-templated carbon, which was recently theorized. Our method allows representing with satisfactory accuracy and a considerably reduced computational effort the reference systems while providing new interesting physical insights in terms of static and diffusive properties.
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We propose a new environment for information encoding and transmission via a novel type of molecular Quantum Dot Cellular Automata (QCA) wire, composed of a single row of head-to-tail interacting 2-dots molecular switches. While most of the research in the field refers to dots-bearing molecules bound on some type of surface, forming a bidimensional array of square cells capable of performing QCA typical functions, we propose here to embed the information bearing elements within the channels of a microporous matrix. In this way molecules would self-assemble in a row as a consequence of adsorption inside the pores of the material, forming an encased wire, with the crystalline environment giving stability and protection to the structure. DFT calculations on a diferrocenyl carborane, previously proposed and synthesized in the literature, were performed both in vacuum and inside the channels of zeolite ITQ-51, indicating that information encoding and trasmission is possible within the nanoconfined environment.
Anomalous short- and long-time self-diffusion of non-overlapping fractal particles on a percolation cluster with spreading dimension $1.67(2)$ is studied by dynamic Monte Carlo simulations. As reported in Phys. Rev. Lett. 115, 097801 (2015), the disordered phase formed by these particles is that of an unconfined, homogeneous and monodisperse fluid in fractal space. During particle diffusion in thermodynamic equilibrium, the mean squared chemical displacement increases as a nonlinear power of time, with an exponent of $0.96(1)$ at short times and $0.63(1)$ at long times. At finite packing fractions the steric hindrance among nearest neighbor particles leads to a sub-diffusive regime that separates short-time anomalous diffusion from long-time anomalous diffusion. Particle localization is observed over eight decades in time for packing fractions of $sim 60%$ and higher.
Molecular dynamics (MD) simulations are used to investigate $^1$H nuclear magnetic resonance (NMR) relaxation and diffusion of bulk $n$-C$_5$H$_{12}$ to $n$-C$_{17}$H$_{36}$ hydrocarbons and bulk water. The MD simulations of the $^1$H NMR relaxation times $T_{1,2}$ in the fast motion regime where $T_1 = T_2$ agree with measured (de-oxygenated) $T_2$ data at ambient conditions, without any adjustable parameters in the interpretation of the simulation data. Likewise, the translational diffusion $D_T$ coefficients calculated using simulation configurations are well-correlated with measured diffusion data at ambient conditions. The agreement between the predicted and experimentally measured NMR relaxation times and diffusion coefficient also validate the forcefields used in the simulation. The molecular simulations naturally separate intramolecular from intermolecular dipole-dipole interactions helping bring new insight into the two NMR relaxation mechanisms as a function of molecular chain-length (i.e. carbon number). Comparison of the MD simulation results of the two relaxation mechanisms with traditional hard-sphere models used in interpreting NMR data reveals important limitations in the latter. With increasing chain length, there is substantial deviation in the molecular size inferred on the basis of the radius of gyration from simulation and the fitted hard-sphere radii required to rationalize the relaxation times. This deviation is characteristic of the local nature of the NMR measurement, one that is well-captured by molecular simulations.
We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.
We outline how auxiliary-field quantum Monte Carlo (AFQMC) can leverage graphical processing units (GPUs) to accelerate the simulation of solid state sytems. By exploiting conservation of crystal momentum in the one- and two-electron integrals we show how to efficiently formulate the algorithm to best utilize current GPU architectures. We provide a detailed description of different optimization strategies and profile our implementation relative to standard approaches, demonstrating a factor of 40 speed up over a CPU implementation. With this increase in computational power we demonstrate the ability of AFQMC to systematically converge solid state calculations with respect to basis set and system size by computing the cohesive energy of Carbon in the diamond structure to within 0.02 eV of the experimental result.
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