Simulation Of Chemical Systems Research Articles

Molecular structure design and interface behavior of ionic liquids on metal surfaces: A theoretical study

The electrodeposition of Mo or Ni, a traditional and mature technique, is of vital importance in industrial applications owing to the extraordinary mechanical properties of these elements when used as assisted coatings. The exploration of green electrodeposition technology has attracted much attention, especially with ionic liquids (ILs) emerging as promising electrolytes or additives owing to their particular physical and electrochemical properties. Many experimental studies have been conducted in this field; however, theoretical calculation, which offers an effective and low-cost approach to designing and choosing appropriate ILs, has rarely been applied. With the rapid development of computational chemistry, calculation, modeling, and simulation of chemical systems have been an important technique to understand and predict behaviors at the molecular level. Combining computational chemistry with experimental study has emerged as a highly efficient methodology for accelerating the development of traditional experimental studies. In this work, the chemical properties of pyridine-base ILs are investigated by density functional theory for the prediction of their potential adsorption performance. Molecular dynamic (MD) simulations of the interfacial behavior between the designed ILs and Ni and Mo surfaces were carried out to evaluate the dynamic processes. In this way, potential ILs as high performance electrolytes and additives for electrodeposition can be chosen on a theoretical basis. The study can be regarded as providing theoretical guidance for choosing appropriate electrolytes or additives for electrodeposition as well as for understanding the interfacial behavior between ILs and metal surfaces.

Second-Order Analytic Derivatives for XYG3 Type of Doubly Hybrid Density Functionals: Theory, Implementation, and Application to Harmonic and Anharmonic Vibrational Frequency Calculations.

Analytic derivative methods in quantum chemistry are powerful tools for the calculation of molecular properties and simulation of chemical systems. While the derivatives of the well-established B2PLYP type of doubly hybrid (DH) density functionals can be generated by a straightforward combination between the Kohn-Sham density functional and the second-order perturbation theory (PT2), both of these two contributions have to be considered nonvariationally for the XYG3 type of DH functionals (xDHs). A total Lagrangian that includes both parts is therefore needed for the corresponding Z-vector equations for the first-order derivatives of xDHs. Starting from the differentiation of the Z-vector equations, a theory for the second-order derivatives for xDHs is developed here and is applied to the molecular harmonic and anharmonic vibrational frequency calculations. The results are generally of high quality, as compared to the well-established experimental and CCSD(T) counterparts. Further investigations on the fundamental frequency predictions prove the capability of the xDH functionals for an accurate calculation of spectroscopic properties for a wide range of medium-size molecules.

Utilization of efficient gradient and Hessian computations in the force field optimization process of molecular simulations

Computer simulations of chemical systems, especially systems of condensed matter, are highly important for both scientific and industrial applications. Thereby, molecular interactions are modeled on a microscopic level in order to study their impact on macroscopic phenomena. To be capable of predicting physical properties quantitatively, accurate molecular models are indispensable. Molecular interactions are described mathematically by force fields, which have to be parameterized. Recently, an automated gradient-based optimization procedure was published by the authors based on the minimization of a loss function between simulated and experimental physical properties. The applicability of gradient-based procedures is not trivial at all because of two reasons: firstly, simulation data are affected by statistical noise, and secondly, the molecular simulations required for the loss function evaluations are extremely time-consuming. Within the optimization process, gradients and Hessians were approximated by finite differences so that additional simulations for the respective modified parameter sets were required. Hence, a more efficient approach to computing gradients and Hessians is presented in this work. The method developed here is based on directional instead of partial derivatives. It is compared with the classical computations with respect to computation time. Firstly, molecular simulations are replaced by fit functions that define a functional dependence between specific physical observables and force field parameters. The goal of these simulated simulations is to assess the new methodology without much computational effort. Secondly, it is applied to real molecular simulations of the three chemical substances phosgene, methanol and ethylene oxide. It is shown that up to 75% of the simulations can be avoided using the new algorithm.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

AESS: Accelerated Exact Stochastic Simulation

The Stochastic Simulation Algorithm (SSA) developed by Gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulation Algorithm (AESS) contains optimized implementations of Gillespieʼs SSA that improve the performance of individual simulation runs or ensembles of simulations used for sweeping parameters or to provide statistically significant results. Program summary Program title: AESS Catalogue identifier: AEJW_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEJW_v1_0.html Program obtainable from: CPC Program Library, Queenʼs University, Belfast, N. Ireland Licensing provisions: University of Tennessee copyright agreement No. of lines in distributed program, including test data, etc.: 10 861 No. of bytes in distributed program, including test data, etc.: 394 631 Distribution format: tar.gz Programming language: C for processors, CUDA for NVIDIA GPUs Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators. Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS Classification: 3, 16.12 Nature of problem: Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespieʼs method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Solution method: The Accelerated Exact Stochastic Simulation (AESS) tool provides implementations of a wide variety of popular variations on the Gillespie method. Users can select the specific algorithm considered most appropriate. Comparisons between the methods and with other available implementations indicate that AESS provides the fastest known implementation of Gillespieʼs method for a variety of test models. Users may wish to execute ensembles of simulations to sweep parameters or to obtain better statistical results, so AESS supports acceleration of ensembles of simulation using parallel processing with MPI, SSE vector units on x86 processors, and/or using NVIDIA GPUs with CUDA.

Exact and Approximate Stochastic Simulation of Intracellular Calcium Dynamics

In simulations of chemical systems, the main task is to find an exact or approximate solution of the chemical master equation (CME) that satisfies certain constraints with respect to computation time and accuracy. While Brownian motion simulations of single molecules are often too time consuming to represent the mesoscopic level, the classical Gillespie algorithm is a stochastically exact algorithm that provides satisfying results in the representation of calcium microdomains. Gillespie's algorithm can be approximated via the tau-leap method and the chemical Langevin equation (CLE). Both methods lead to a substantial acceleration in computation time and a relatively small decrease in accuracy. Elimination of the noise terms leads to the classical, deterministic reaction rate equations (RRE). For complex multiscale systems, hybrid simulations are increasingly proposed to combine the advantages of stochastic and deterministic algorithms. An often used exemplary cell type in this context are striated muscle cells (e.g., cardiac and skeletal muscle cells). The properties of these cells are well described and they express many common calcium-dependent signaling pathways. The purpose of the present paper is to provide an overview of the aforementioned simulation approaches and their mutual relationships in the spectrum ranging from stochastic to deterministic algorithms.

A probabilistic cellular automaton for two dimensional contaminant transport simulation in ground water

In recent years evolutionary computing algorithms have been proposed to solve many engineering problems. Genetic algorithms, Neural Networks, and Cellular Automata are the branches of evolutionary computing techniques. In this study, it is proposed to simulate the contaminant transport in porous media using a Cellular Automaton. The physical processes and chemical reactions occurring in the ground water system are intricately connected at various scales of space, time, transport coefficients and molecular concentration. The validity of continuous approach for the simulation of chemical systems with low concentration of species and intracellular environments has become subtle. Due to the difference in scales of various processes that occur in the ground water system, the description of the system can be well defined in the intermediate scale called mesoscopic scale, which is in between microscopic and macroscopic description. Mesoscopic models provide the relationship between various parameters and their evolvement in time, thus establishing the contact between modeling at various scales at the interface. In this paper, a Probabilistic Cellular Automaton (PCA) model has been developed based on the transport and reaction probability values. The developed model was verified and validated for one, two dimensional transport systems and also for the simulation of BTEX transport in two dimensional system in ground water.

The role of density functional theory in chemistry: Some historical landmarks and applications to zeolites

Density functional theory (DFT) has progressively emerged in the last 40 years as a leading methodology for the modelling and simulation of chemical systems. In this paper, some historical landmarks in the development of this method are outlined, emphasizing on its main characteristic being an electron density-based theory. This is in contrast with wavefunction-based methodologies which were exclusively employed previously. Interestingly, DFT has been first applied to solids, with a rather late recognition by chemists and molecular scientists. After this historical survey, several applications of DFT to the structure and properties of zeolites are reviewed as a tribute to Dr Annick Goursot.

8] Simulations of complex chemical systems

Publisher Summary This chapter discusses the problem of realistically simulating complex of chemical systems from the points of view of present theories, application softwares, and system hardwares. It also discusses a capable approach of simulating nontrivial systems. A new supercomputer, ICAP, loosely coupled array of processors, which is assembled and which appears to be notably well adapted to the needs of computational chemistry is described in the chapter. The research has a two-prong strategy: (1) experiment on hardware and system software to bring about new concepts and designs in supercomputers and (2) use them to solve complex problems mainly from the area of biophysics. A simulation of a chemical system is staged into three successive steps. First, by ab initio quantum mechanical methods, the energetic and structural characteristic of its separated molecules are computed. Second step, the computed intermolecular interaction energies are used to construct two-body atom–atom interaction potentials. Third step use these analytical potentials as an input for statistical mechanical simulations. The simulations are not restricted to quantum chemical modeling, but include both Monte Carlo and molecular dynamics as the aspects of statistical mechanics.