Search

The surface roughness of metal nanoparticles is known to be influential toward their properties, but the quantification of surface roughness is challenging. Given the recent availability of large‐scale simulated data and tools for the computation of the box‐counting dimension of simulated atomistic objects, researchers are now enabled to study the connections between the surface roughness of metal nanoparticles and their properties. Herein, the relationships between the fractal box‐counting dimension of metal nanoparticle surfaces and structural features relevant to experimental and computational studies are investigated, providing actionable insights for the manufacturing of rough nanoparticles. This approach differs from conventional concepts of roughness, but introduces a possible indicator for their functionalities such as catalytic performance that was not previously accessible. It is found that, while it remains difficult to consistently correlate the dimension with the catalytic activity of surface facets, matching trends with their surface energy, thermodynamic stability, and number of bond vacancy are observed. This highlights the potential of fractal box‐counting dimensions to rationalize catalytic activity trends among metal nanoparticles, and opens up opportunities for the design of nanocatalysts with better performance via surface engineering. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The problem of feature extraction, in crystalline solid‐state systems with point defects, is considered. Novel methods for creating features for use in machine‐learning‐based predictive modeling of such systems are developed. The methods are illustrated in a case study where machine learning methods are used to predict the onset of amorphization in crystalline systems containing vacancy defects. How the methods developed may be generalized to study other problems in solid‐state materials is also discussed. [ABSTRACT FROM AUTHOR]

Abinitio models of GexAs10Se90–x, and GexSb10Se90–xglassesare constructed, and their bonding environments are characterizedand compared against each other and to recent experimental studiesof equivalent glasses at the same stoichiometry and density. A minimumin the linear refractive index is found to correlate with a maximumin the number of length-one, predominantly Se, atomic chains for bothglass types. The threshold behavior difference between GeAsSe andGeSbSe is shown to be due to the appearance of As–As–Se2structural units beyond the MCN = 2.67 threshold in the GeAsSeglasses. [ABSTRACT FROM AUTHOR]

Ternaryglass structures are notoriously difficult to model accurately,and yet prevalent in several modern endeavors. Here, a novel combinationof Reverse Monte Carlo (RMC) modeling and ab initio molecular dynamics(MD) is presented, rendering these complicated structures computationallytractable. A case study (Ge6.25As32.5Se61.25glass) illustrates the effects of ab initio MD quenchrates and equilibration temperatures, and the combined approach’sefficacy over standard RMC or random insertion methods. Submeltingpoint MD quenches achieve the most stable, realistic models, agreeingwith both experimental and fully ab initio results. The simple approachof RMC followed by ab initio geometry optimization provides similarquality to the RMC–MD combination, for far fewer resources. [ABSTRACT FROM AUTHOR]