George Opletal

Hybrid approach for generating realistic amorphous carbon structure using metropolis and reverse Monte Carlo

An improved method for the modelling of carbon structures based on a hybrid reverse Monte Carlo (HRMC) method is presented. This algorithm incorporates an accurate environment dependent interaction potential (EDIP) in conjunction with the commonly used constraints derived from experimental data. In this work, we compare this new method with other modelling results for a small system of 2.9 g/cc amorphous carbon. We find that the new approach greatly improves the structural description, alleviating the common problem in standard reverse Monte Carlo method (RMC) of generating structures with a high proportion of unphysical small rings. The advantage of our method is that larger systems can now be modelled, allowing the incorporation of mesoscopic scale features.

Structural analysis of carbonaceous solids using an adapted reverse Monte Carlo algorithm

We present microstructural analysis of a disordered carbonaceous solid using simulations that employ a modified reverse Monte Carlo (RMC) algorithm. This algorithm incorporates an accurate environment dependent interaction potential (EDIP) in addition to commonly used constraints derived from experimental data, such as the sp2/sp3 bonding ratio. Our approach improves the microstructural description for carbon, alleviating the common problem in standard RMC of generating structures with large proportions of highly strained and physically unreasonable small rings. We also compare the electron diffraction data used in the modified RMC algorithm to our recent results from a neutron diffraction investigation of the carbonaceous material under consideration.

The structure of disordered carbon solids studied using a hybrid reverse Monte Carlo algorithm

A hybrid reverse Monte Carlo (HRMC) algorithm, which incorporates both experimental and energy based constraints, is applied to investigate the microstructure of two disordered carbons of vastly different densities and bonding. We have developed a novel liquid quench procedure which in combination with the HRMC algorithm accurately describes the structure of these solids. Atomic networks generated by this approach are consistent with experimental and ab initio results and the method has been shown to overcome common difficulties associated with alternative approaches for modelling these complex systems. This procedure produces realistic large scale atomic structures which give a detailed picture of the structure of these solids.

Crystallization in suspensions of hard spheres: A Monte Carlo and Molecular Dynamics simulation study

The crystallization of a metastable melt is one of the most important non-equilibrium phenomena in condensed matter physics, and hard sphere colloidal model systems have been used for several decades to investigate this process by experimental observation and computer simulation. Nevertheless, there is still an unexplained discrepancy between the simulation data and experimental nucleation rate densities. In this paper we examine the nucleation process in hard spheres using molecular dynamics and Monte Carlo simulation. We show that the crystallization process is mediated by precursors of low orientational bond-order and that our simulation data fairly match the experimental data sets.

HRMC_2.0: Hybrid Reverse Monte Carlo method with silicon, carbon and germanium potentials

The Hybrid Reverse Monte Carlo (HRMC) code models the atomic structure of materials via the use of a combination of constraints including experimental diffraction data and an empirical energy potential. In this version update, germanium potential parameters are introduced and constraints based on the coordination, average coordination and the total bond angle distribution are implemented. Other additional changes include a constraint on three member ring formation, a constraint on porosity and an extension to handle systems with up to three different elements.

Chiminey: reliable computing and data management platform in the cloud

The enabling of scientific experiments that are embarrassingly parallel, long running and data-intensive into a cloud-based execution environment is a desirable, though complex undertaking for many researchers. The management of such virtual environments is cumbersome and not necessarily within the core skill set for scientists and engineers. We present here Chiminey, a software platform that enables researchers to (i) run applications on both traditional high-performance computing and cloud-based computing infrastructures, (ii) handle failure during execution, (iii) curate and visualise execution outputs, (iv) share such data with collaborators or the public, and (v) search for publicly available data.

Modeling of structure and porosity in amorphous silicon systems using Monte Carlo methods.

Porous solids are very important from a scientific point of view as they provide a medium in which to study the behavior of confined fluids. Although some porous solids have a well defined pore geometry such as zeolites, many porous solids lack crystalline order and are usually described as amorphous. The description of the pore geometry in such structures is very difficult. The authors develop a modeling approach using a Monte Carlo algorithm to simulate porosity within amorphous systems based on constraints for the internal volume and surface area. To illustrate this approach, a model of microporous amorphous silicon is presented. Structural aspects of the porous model are then compared against hybrid reverse Monte Carlo simulations of nonporous amorphous silicon and published results from the literature. It is found that coordination defects are predominately located at the pore surface walls.

Bonding trends within ternary isocoordinate chalcogenide glasses GexAsySe1−x−y

A structural study is presented of ab initio molecular dynamics simulations of Ge-As-Se chalcogenide glasses performed at the same mean coordination number but differing stoichiometry ranging between Se rich and Se poor glasses. Starting configurations are generated via Reverse Monte Carlo (RMC) simulations of Extended X-ray Absorption Fine Structure (EXAFS) measurements of experimental samples. Structural analysis is presented illustrating the bonding trends found with changing stoichiometry.

Constructing ab initio models of ultra-thin Al–AlOx–Al barriers

The microscopic structure of ultra-thin oxide barriers often plays a major role in modern nano-electronic devices. In the case of superconducting electronic circuits, their operation depends on the electrical nonlinearity provided by one or more such oxide layers in the form of ultra-thin tunnel barriers (also known as Josephson junctions). Currently available fabrication techniques manufacture an amorphous oxide barrier, which is attributed as a major noise source within the device. The nature of this noise is currently an open question and requires both experimental and theoretical investigation. Here, we present a methodology for constructing atomic-scale computational models of Josephson junctions using a combination of molecular mechanics, empirical and ab initio methods. These junctions consist of ultra-thin amorphous aluminium-oxide layers sandwiched between crystalline aluminium. The stability and structure of these barriers as a function of density and stoichiometry are investigated, which we compare with experimentally observed parameters.

Scalable and Fault-Tolerant Cloud Computations: Modelling and Implementation

This paper presents a formal model for science clouds, capable of predicting and controlling resources scalably, as well as its implementation as an open source solution, called Chiminey. The feasibility of Chiminey is shown using case studies on biophysics and structural chemistry computations. Big data is acquired from scientific instruments such as synchrotrons and atomic force microscopes. The model takes into account the architecture of the overall parallel and distributed system including large-scale data sources; data sinks, for example petabyte research data stores; and cluster or cloud virtual resources and infrastructures characterised by users in simple parameters upfront. Chiminey is developed to control large numbers of processes and to provide a reliable computing and data management, which can be used by researchers without having to learn extensive infrastructure concepts and technologies.