Precursor-mediated crystallization process in suspensions of hard spheres

We report on a large scale computer simulation study of crystal nucleation in hard spheres. Through a combined analysis of real and reciprocal space data, a picture of a two-step crystallization process is supported: First dense, amorphous clusters form which then act as precursors for the nucleation of well-ordered crystallites. This kind of crystallization process has been previously observed in systems that interact via potentials that have an attractive as well as a repulsive part, most prominently in protein solutions. In this context the effect has been attributed to the presence of metastable fluid-fluid demixing. Our simulations, however, show that a purely repulsive system (that has no metastable fluid-fluid coexistence) crystallizes via the same mechanism.Comment: 4 figure

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 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

Pressure-induced huge increase of Curie temperature of the van der Waals ferromagnet VI3

Evolution of magnetism in single crystals of the van der Waals compound VI3 in external pressure up to 7.3 GPa studied by measuring magnetization and ac magnetic susceptibility is reported. Four magnetic phase transitions, at T1 = 54.5 K, T2 = 53 K, TC = 49.5 K, and TFM = 26 K, respectively have been observed at ambient pressure. The first two have been attributed to the onset of ferromagnetism in specific crystal-surface layers. The bulk ferromagnetism is characterized by the magnetic ordering transition at Curie temperature TC and the transition between two different ferromagnetic phases TFM, accompanied by a structure transition from monoclinic to triclinic symmetry upon cooling. The pressure effects on magnetic parameters were studied with three independent techniques. TC was found to be almost unaffected by pressures up to 0.6 GPa whereas TFM increases rapidly with increasing pressure and reaches TC at a triple point at ~ 0.85 GPa. At higher pressures, only one magnetic phase transition is observed moving to higher temperatures with increasing pressure to reach 99 K at 7.3 GPa. In contrast, the low-temperature bulk magnetization is dramatically reduced by applying pressure (by more than 50% at 2.5 GPa) suggesting a possible pressure-induced reduction of vanadium magnetic moment. We discussed these results in light of recent theoretical studies to analyze exchange interactions and provide how to increase the Curie temperature of VI3.Comment: 20 pages, 16 figure

Investigation of bonding within ab initio models of GeAsSe glasses

A study is presented into the structural details of ab initio molecular dynamics simulations of GeAsSe chalcogenide glasses with ideal stoichiometry over a range of mean coordination numbers (MCN). The structural variability dependence upon initial starting structure, robustness of the 8-N rule and trends in the dominant bonding environments are investigated and compared to recently published models

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

Investigation of bonding within ab initio models of GeAsSe glasses

A study is presented into the structural details of ab initio molecular dynamics simulations of GeAsSe chalcogenide glasses with ideal stoichiometry over a range of mean coordination numbers (MCN). The structural variability dependence upon initial start

Structural modeling of Ge6.25As32.5Se61.25 using a combination of reverse monte carlo and Ab initio molecular dynamics

Ternary glass structures are notoriously difficult to model accurately, and yet prevalent in several modern endeavors. Here, a novel combination of Reverse Monte Carlo (RMC) modeling and ab initio molecular dynamics (MD) is presented, rendering these complicated structures computationally tractable. A case study (Ge6.25As32.5Se61.25 glass) illustrates the effects of ab initio MD quench rates and equilibration temperatures, and the combined approachs efficacy over standard RMC or random insertion methods. Submelting point MD quenches achieve the most stable, realistic models, agreeing with both experimental and fully ab initio results. The simple approach of RMC followed by ab initio geometry optimization provides similar quality to the RMC-MD combination, for far fewer resources

Modeling the crystallization of gold nanoclusters - The effect of the potential energy function.

The crystallization dynamics of 5083 atom gold nanoclusters, which were quenched from the melt, were studied by molecular dynamics (MD) using the EAM 'Glue' and 'Force-matched' potentials to compare and contrast how the crystallization dynamics is affected by these potential energy functions. MD simulations from each potential showed the formation of gold nanoclusters of icosahedral morphology during the quenching process, which is in good agreement with the experimental studies of gold nanoclusters formed under vacuum. The effect of the potential on the evolution of cluster (surface and interior) morphology during the crystallization process is discussed