kmos: A lattice kinetic Monte Carlo framework

Kinetic Monte Carlo (kMC) simulations have emerged as a key tool for microkinetic modeling in heterogeneous catalysis and other materials applications. Systems, where site-specificity of all elementary reactions allows a mapping onto a lattice of discrete active sites, can be addressed within the particularly efficient lattice kMC approach. To this end we describe the versatile kmos software package, which offers a most user-friendly implementation, execution, and evaluation of lattice kMC models of arbitrary complexity in one- to three-dimensional lattice systems, involving multiple active sites in periodic or aperiodic arrangements, as well as site-resolved pairwise and higher-order lateral interactions. Conceptually, kmos achieves a maximum runtime performance which is essentially independent of lattice size by generating code for the efficiency-determining local update of available events that is optimized for a defined kMC model. For this model definition and the control of all runtime and evaluation aspects kmos offers a high-level application programming interface. Usage proceeds interactively, via scripts, or a graphical user interface, which visualizes the model geometry, the lattice occupations and rates of selected elementary reactions, while allowing on-the-fly changes of simulation parameters. We demonstrate the performance and scaling of kmos with the application to kMC models for surface catalytic processes, where for given operation conditions (temperature and partial pressures of all reactants) central simulation outcomes are catalytic activity and selectivities, surface composition, and mechanistic insight into the occurrence of individual elementary processes in the reaction network.Comment: 21 pages, 12 figure

First-Principles Approach to Heat and Mass Transfer Effects in Model Catalyst Studies

We assess heat and mass transfer limitations in in situ studies of model catalysts with a first-principles based multiscale modeling approach that integrates a detailed description of the surface reaction chemistry and the macro-scale flow structures. Using the CO oxidation at RuO2(110) as a prototypical example we demonstrate that factors like a suppressed heat conduction at the backside of the thin single-crystal, and the build-up of a product boundary layer above the flat-faced surface play a significant role

Predictive-Quality Surface Reaction Chemistry in Real Reactor Models: Integrating First-Principles Kinetic Monte Carlo Simulations into Computational Fluid Dynamics

We present a numerical framework to integrate first-principles kinetic Monte Carlo (1p-kMC) based microkinetic models into the powerful computational fluid dynamics (CFD) package CatalyticFoam. This allows for the simultaneous account of a predictive-quality surface reaction kinetics inside an explicitly described catalytic reactor geometry. Crucial means toward an efficient and stable implementation are the exploitation of the disparate time scales of surface chemistry and gas-phase transport, as well as the reliable interpolation of irregularly gridded 1p-kMC data by means of an error-based modified Shepard approach. We illustrate the capabilities of the framework using the CO oxidation at Pd(100) and RuO2(110) model catalysts in different reactor configurations and fluid dynamic conditions as showcases. These showcases underscore both the necessity and value of having reliable treatments of the surface chemistry and flow inside integrated multiscale catalysis simulations when aiming at an atomic-scale understanding of the catalytic function in near-ambient environments. Our examples highlight how intricately this function is affected by specifics of the reactor geometry and heat dissipation channels on the one end, and on the other end by characteristics of the intrinsic catalytic activity that are only captured by treatments beyond prevalent mean-field rate equations

The moderating effects of mindful eating on the relationship between emotional functioning and eating styles in overweight and obese women

The aim of the current study was to examine the moderating effect of mindful eating on the relationship between emotional functioning and eating styles in overweight and obese women. One hundred and eighty four overweight and obese adult women (BMI 30.12\u2009\ub1\u20093.77 kg/m2) were assessed with the Difficulties in Emotion Regulation Scale, the Positive and Negative Affect Schedule, the Three Factor Eating Questionnaire and the Mindful Eating Scale. Mindful eating significantly moderated several of the relationships between emotional functioning and eating styles. When mindful eating techniques are included as part of an intervention for overweight or obese individuals, it is even more important that those interventions should also include techniques to reduce emotion dysregulation and negative affect

Equilibrium initial data for moving puncture simulations: The stationary 1+log slicing

We propose and explore a "stationary 1+log" slicing condition for the construction of solutions to Einstein's constraint equations. For stationary spacetimes, these initial data will give a stationary foliation when evolved with "moving puncture" gauge conditions that are often used in black hole evolutions. The resulting slicing is time-independent and agrees with the slicing generated by being dragged along a time-like Killing vector of the spacetime. When these initial data are evolved with moving puncture gauge conditions, numerical errors arising from coordinate evolution are minimized. In the construction of initial data for binary black holes it is often assumed that there exists an approximate helical Killing vector that generates the binary's orbit. We show that, unfortunately, 1+log slices that are stationary with respect to such a helical Killing vector cannot be asymptotically flat, unless the spacetime possesses an additional axial Killing vector.Comment: 20 pages, 3 figures, published versio

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm's applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained. The results demonstrate the algorithm's vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal

Two distinct arginine methyltransferases are required for biogenesis of Sm-class ribonucleoproteins

Small nuclear ribonucleoproteins (snRNPs) are core components of the spliceosome. The U1, U2, U4, and U5 snRNPs each contain a common set of seven Sm proteins. Three of these Sm proteins are posttranslationally modified to contain symmetric dimethylarginine (sDMA) residues within their C-terminal tails. However, the precise function of this modification in the snRNP biogenesis pathway is unclear. Several lines of evidence suggest that the methyltransferase protein arginine methyltransferase 5 (PRMT5) is responsible for sDMA modification of Sm proteins. We found that in human cells, PRMT5 and a newly discovered type II methyltransferase, PRMT7, are each required for Sm protein sDMA modification. Furthermore, we show that the two enzymes function nonredundantly in Sm protein methylation. Lastly, we provide in vivo evidence demonstrating that Sm protein sDMA modification is required for snRNP biogenesis in human cells

The double-edged effect of intergroup similarity: Muslim and Christian immigrants’ acculturation preferences on intergroup relations in Sweden

A 2x2x2 experiment examined effects of the acculturation orientations seen to be endorsed by immigrants (of two different religions) on intergroup relations in Sweden. Swedish majority participants (N = 448) read interviews with Iraqi immigrants in which the immigrants’ religion (Muslim vs. Christian), desired level of contact with the host society (high vs. low) and desire to maintain their own culture (high vs. low) were manipulated. Overall, immigrants who were perceived to favour contact elicited more favourable intergroup attitudes. Desire for contact also interacted with immigrants’ religion: contact among Muslim minorities increased majority members’ support for multiculturalism. In addition, majority members identified more with being Swedish when Christian minorities appeared to endorse contact and reject their heritage culture, which corresponds to an acculturation strategy of assimilation. These findings demonstrate the complex role of religious similarity in intergroup relations. Implications for future research are proposed

A practical approach to the sensitivity analysis for kinetic Monte Carlo simulation of heterogeneous catalysis

Lattice kinetic Monte Carlo simulations have become a vital tool for predictive quality atomistic understanding of complex surface chemical reaction kinetics over a wide range of reaction conditions. In order to expand their practical value in terms of giving guidelines for the atomic level design of catalytic systems, it is very desirable to readily evaluate a sensitivity analysis for a given model. The result of such a sensitivity analysis quantitatively expresses the dependency of the turnover frequency, being the main output variable, on the rate constants entering the model. In the past, the application of sensitivity analysis, such as degree of rate control, has been hampered by its exuberant computational effort required to accurately sample numerical derivatives of a property that is obtained from a stochastic simulation method. In this study, we present an efficient and robust three-stage approach that is capable of reliably evaluating the sensitivity measures for stiff microkinetic models as we demonstrate using the CO oxidation on RuO2(110) as a prototypical reaction. In the first step, we utilize the Fisher information matrix for filtering out elementary processes which only yield negligible sensitivity. Then we employ an estimator based on the linear response theory for calculating the sensitivity measure for non- critical conditions which covers the majority of cases. Finally, we adapt a method for sampling coupled finite differences for evaluating the sensitivity measure for lattice based models. This allows for an efficient evaluation even in critical regions near a second order phase transition that are hitherto difficult to control. The combined approach leads to significant computational savings over straightforward numerical derivatives and should aid in accelerating the nano-scale design of heterogeneous catalysts

Relativistic Hartree-Bogoliubov Approach for Nuclear Matter with Non-Linear Coupling Terms

We investigate the pairing property of nuclear matter with Relativistic Hartree-Bogoliubov(RHB) approach. Recently, the RHB approach has been widely applied to nuclear matter and finite nuclei. We have extended the RHB approach to be able to include non-linear coupling terms of mesons. In this paper we apply it to nuclear matter and observe the effect of non-linear terms on pairing gaps.Comment: 13 pages, 5 figure