Bonding trends within ternary Isocoordinate chalcogenide glasses GeAsSe

A structural study is presented of ab-initio molecular dynamics simulations of Ge-As-Se calcogenide 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.Comment: 26 pages, 9 figures. Submitted to Physical Review

Simulating facet-dependent aggregation and assembly of distributions of polyhedral nanoparticles

Coarse-grained molecular dynamics simulations of diamond nanoparticles were performed to investigate the effects of size polydispersity on three polyhedral shapes chosen to span a diverse space of surface interactions. It was found that the resulting self-assembly was size dependent as the simulations were quenched, with the largest nanoparticles providing a clustered scaffold for subsequent smaller nanoparticle assembly. Additionally, facet-facet interactions were dominated by the {111} surface and the resulting aggregate was dominated by meso-sized porosity for monodisperse systems, broadening to larger diameters for polydisperse systems.Computational resources for this project have been supplied by the National Computing Infrastructure (NCI) national facility under partner Grant q27

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.Comment: Preprint, ICSE 201

Simulating Facet‐Dependent Aggregation and Assembly of Mixtures of Polyhedral Nanoparticles

The functionality of many nanomaterials involves the collective properties of aggregates and self-assembled superstructures. Using the mesostructure as a design parameter requires predictive capabilities, but this is challenging when nanoparticle samples present a diverse mixture of shapes and surface facets. Molecular dynamics with over 5000 nanoparticles is used here to simulate different mixtures of polyhedral nanodiamonds, with surfaces indicative of their characteristic multipolar surface electrostatic potential. It is found that polydispersed samples have different aggregation behavior than monodispersed samples, particularly in regard to the interparticle coordination and degree of long-range order, but mixing nanoparticle shapes does not affect aggregate porosity

Selecting machine learning models for metallic nanoparticles

The outcome of machine learning is influenced by the features used to describe the data, and various metrics are used to measure model performance. In this study we use five different feature sets to describe the same 4000 gold nanoparticles, and 14 different machine learning methods to compare a total of 70 high scoring models. We then use classification and regression to show which meta-features of data sets or machine learning algorithms are important when making a selection. We find that number of features, and those that are strongly correlated, determine the class of model that should be used, but overall quality is almost entirely determined by the cross-validation score, regardless of the sophistication of the algorithm

Simulated nanoparticle assembly using protoparticles (SNAP)

Many functional properties of particle system rely on collective behaviour and the type of superstructures formed when thousands of particles come together. Self-assembly, agglomeration and aggregation depend sensitively on the size and shape of particles present, as well as the size distribution and the mixtures of shapes within a given sample, which makes simulation of these superstructures and their properties challenging. Here we present a new, flexible, software package for the simulations of ordered and disordered aggregates of faceted polyhedral particle from the nanoscale to the micron-scale, which is capable of including size distributions and mixtures of multiple particle shapes defined by the User, subject to additional User-defined interactions. Following relaxation using molecular dynamics a number of characterisation tools are provided, including interfacial probabilities and distribution functions. The software is applicable to a range of problems from nanoparticle assembly to additive manufacturing

Classification of Platinum Nanoparticle Catalysts using Machine Learning

Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximise the performance on a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction (ORR), hydrogen oxidation (HOR) and hydrogen evolution (HER) reactions. By including classification prior to regression we identified two distinct classes of nanoparticles, and subsequently generate the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab

The pure and representative types of disordered platinum nanoparticles from machine learning

The development of interpretable structure/property relationships is a cornerstone of nanoscience, but can be challenging when the structural diversity and complexity exceeds our ability to characterise it. This is often the case for imperfect, disordered and amorphous nanoparticles, where even the nomenclature can be unspecific. Disordered platinum nanoparticles have exhibited superior performance for some reactions, which makes a systematic way of describing them highly desirable. In this study we have used a diverse set of disorder platinum nanoparticles and machine learning to identify the pure and representative structures based on their similarity in 121 dimensions. We identify two prototypes that are representative of separable classes, and seven archetypes that are the pure structures on the convex hull with which all other possibilities can be described. Together these nine nanoparticles can explain all of the variance in the set, and can be described as either single crystal, twinned, spherical or branched; with or without roughened surfaces. This forms a robust sub-set of platinum nanoparticle upon which to base further work, and provides a theoretical basis for discussing structure/property relationships of platinum nanoparticles that are not geometrically ideal