A new algorithm for electrostatic interactions in Monte Carlo simulations of charged particles

To minimise systematic errors in Monte Carlo simulations of charged particles, long range electrostatic interactions have to be calculated accurately and efficiently. Standard approaches, such as Ewald summation or the naive application of the classical Fast Multipole Method, result in a cost per Metropolis-Hastings step which grows in proportion to some positive power of the number of particles N in the system. This prohibitively large cost prevents accurate simulations of systems with a sizeable number of particles. Currently, large systems are often simulated by truncating the Coulomb potential which introduces uncontrollable systematic errors. In this paper we present a new multilevel method which reduces the computational complexity to O(log(N)) per Metropolis-Hastings step, while maintaining errors which are comparable to direct Ewald summation. We show that compared to related previous work, our approach reduces the overall cost by better balancing time spent in the proposal- and acceptance- stages of each Metropolis-Hastings step. By simulating large systems with up to N=10^5 particles we demonstrate that our implementation is competitive with state-of-the-art MC packages and allows the simulation of very large systems of charged particles with accurate electrostatics

A new algorithm for electrostatic interactions in Monte Carlo simulations of charged particles

To minimise systematic errors in Monte Carlo simulations of charged particles, long range electrostatic interactions have to be calculated accurately and efficiently. Standard approaches, such as Ewald summation or the naive application of the classical Fast Multipole Method, result in a cost per Metropolis-Hastings step which grows in proportion to some positive power of the number of particles $N$ in the system. This prohibitively large cost prevents accurate simulations of systems with a sizeable number of particles. Currently, large systems are often simulated by truncating the Coulomb potential which introduces uncontrollable systematic errors. In this paper we present a new multilevel method which reduces the computational complexity to $\mathcal{O}(\log(N))$ per Metropolis-Hastings step, while maintaining errors which are comparable to direct Ewald summation. We show that compared to related previous work, our approach reduces the overall cost by better balancing time spent in the proposal- and acceptance- stages of each Metropolis-Hastings step. By simulating large systems with up to $N=10^5$ particles we demonstrate that our implementation is competitive with state-of-the-art MC packages and allows the simulation of very large systems of charged particles with accurate electrostatics.Comment: 19 pages, 9 figures, 1 tabl

Fast electrostatic solvers for kinetic Monte Carlo simulations

Kinetic Monte Carlo (KMC) is an important computational tool in physics and chemistry. In contrast to standard Monte Carlo, KMC permits the description of time dependent dynamical processes and is not restricted to systems in equilibrium. Recently KMC has been applied successfully in modelling of novel energy materials such as Lithium-ion batteries and solar cells. We consider general solid state systems which contain free, interacting particles which can hop between localised sites in the material. The KMC transition rates for those hops depend on the change in total potential energy of the system. For charged particles this requires the frequent calculation of electrostatic interactions, which is usually the bottleneck of the simulation. To avoid this issue and obtain results in reasonable times, many studies replace the long-range potential by a short range approximation. This, however, leads to systematic errors and unphysical results. On the other hand standard electrostatic solvers such as Ewald summation or fast Poisson solvers are highly inefficient or introduce uncontrollable systematic errors at high resolution. In this paper we describe how the Fast Multipole Method by Greengard and Rokhlin can be adapted to overcome this issue by dramatically reducing computational costs. We exploit the fact that each update in the transition rate calculation corresponds to a single particle move and changes the configuration only by a small amount. This allows us to construct an algorithm which scales linearly in the number of charges for each KMC step, something which had not been deemed to be possible before. We demonstrate the performance and parallel scalability of the method by implementing it in a performance portable software library. We describe the high-level Python interface of the code which makes it easy to adapt to specific cases.Comment: 26 pages, 19 figures, 7 tables; accepted for publication in Computer Physics Communication

Use of Clergy Services among Individuals Seeking Treatment for Alcohol Use Problems

This study examined the prevalence and characteristics of adults with an alcohol use-related problem who receive clergy services. Data come from the National Epidemiologic Survey on Alcohol and Related Conditions. Among persons who sought any services for alcohol-related problems (n = 1,910), 14.7% reported using clergy services. In a multivariable logistic regression model, factors associated with increased likelihood of service use included being Black, aged 35–54 years, a lifetime history of alcohol dependence, major depressive disorder, and personality disorder. Clergy may benefit from training to identify alcohol use problems and serve an important role in making treatment referrals.   (Am J Addict 2010;00:1–7)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79375/1/j.1521-0391.2010.00050.x.pd

A study on automotive drivetrain transient response to ‘clutch abuse’ events

The optimal design of driveline components in passenger vehicles requires detailed knowledge of the effects that load case scenarios introduce into the system. In many cases the latter are difficult to obtain, since a large number of tested cases are required experimentally. Excessive torque loading often occurs during driveline ‘clutch abuse’ events, where the clutch is suddenly engaged and a transient power wave is transmitted across the driveline. This work details the development and validation of a numerical tool, which can be used to simulate such abuse scenarios. The scenario examined consists of a sudden clutch engagement in first gear in a stationary vehicle. The numerical model is validated against experimentally measured torque data, showing fairly good agreement. A set of parametric studies is also carried out using a numerical tool in order to determine the driveline parameters of interest, which affect the generated torque amplitudes

A Domain Specific Language for Performance Portable Molecular Dynamics Algorithms

Developers of Molecular Dynamics (MD) codes face significant challenges when adapting existing simulation packages to new hardware. In a continuously diversifying hardware landscape it becomes increasingly difficult for scientists to be experts both in their own domain (physics/chemistry/biology) and specialists in the low level parallelisation and optimisation of their codes. To address this challenge, we describe a "Separation of Concerns" approach for the development of parallel and optimised MD codes: the science specialist writes code at a high abstraction level in a domain specific language (DSL), which is then translated into efficient computer code by a scientific programmer. In a related context, an abstraction for the solution of partial differential equations with grid based methods has recently been implemented in the (Py)OP2 library. Inspired by this approach, we develop a Python code generation system for molecular dynamics simulations on different parallel architectures, including massively parallel distributed memory systems and GPUs. We demonstrate the efficiency of the auto-generated code by studying its performance and scalability on different hardware and compare it to other state-of-the-art simulation packages. With growing data volumes the extraction of physically meaningful information from the simulation becomes increasingly challenging and requires equally efficient implementations. A particular advantage of our approach is the easy expression of such analysis algorithms. We consider two popular methods for deducing the crystalline structure of a material from the local environment of each atom, show how they can be expressed in our abstraction and implement them in the code generation framework.Comment: 24 pages, 12 figures, 11 tables, accepted for publication in Computer Physics Communications on 12 Nov 201

Sense of belonging in higher education students : an Australian longitudinal study from 2013 to 2019

Student sense of belonging is a current challenge to higher education providers, with consistently declining ratings in national surveys. For universities globally, this is a concern linked to student attrition, student satisfaction, and student success. Importantly, low sense of belonging is typically associated with non-traditional learners, and building strategies to solve this challenge is essential for institutions to build equitable learning environments. This study seeks to understand the causal factors that predict when a student will belong using longitudinal data. Using the Australian national student experience survey data (n = 1,159,768 undergraduate and postgraduate students between 2013 and 2019), this study examines the predictors of a sense of belonging testing the accuracy of four machine learning models. The findings indicate overall educational experience, connection to students outside of class, and support to settle were key predictors, with skill development and curriculum supports a lesser predictor of a sense of belonging. Interestingly, identity and individual differences ratings seemed to have less importance than student experience factors. Implications for higher education policy developers and curriculum writers are considered