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.

Unsupervised structure classes vs. supervised property classes of silicon quantum dots using neural networks.

Machine learning classification is a useful technique to predict structure/property relationships in samples of nanomaterials where distributions of sizes and mixtures of shapes are persistent. The separation of classes, however, can either be supervised based on domain knowledge (human intelligence), or based entirely on unsupervised machine learning (artificial intelligence). This raises the questions as to which approach is more reliable, and how they compare? In this study we combine an ensemble data set of electronic structure simulations of the size, shape and peak wavelength for the optical emission of hydrogen passivated silicon quantum dots with artificial neural networks to explore the utility of different types of classes. By comparing the domain-driven and data-driven approaches we find there is a disconnect between what we see (optical emission) and assume (that a particular color band represents a special class), and what the data supports. Contrary to expectation, controlling a limited set of structural characteristics is not specific enough to classify a quantum dot based on color, even though it is experimentally intuitive.

Machine learning reveals multiple classes of diamond nanoparticles.

Generating samples of nanoparticles with specific properties that allow for structural diversity, rather than requiring structural precision, is a more sustainable prospect for industry, where samples need to be both targeted to specific applications and cost effective. This can be better enabled by defining classes of nanoparticles and characterising the properties of the class as a whole. In this study, we use machine learning to predict the different classes of diamond nanoparticles based entirely on the structural features and explore the populations of these classes in terms of the size, shape, speciation and charge transfer properties. We identify 9 different types of diamond nanoparticles based on their similarity in 17 dimensions and, contrary to conventional wisdom, find that the fraction of sp2 or sp3 hybridized atoms are not strong determinants, and that the classes are only weakly related to size. Each class has been describe in such way as to enable rapid assignment using microanalysis techniques.

Molecular Dynamics Simulations of Star Polymeric Molecules with Diblock Arms, a Comparative Study.

We have performed all atom explicit solvent molecular dynamics simulations of three different star polymeric systems in water, each star molecule consisting of 16 diblock copolymer arms bound to a small adamantane core. The arms of each system consist of an inner "hydrophobic" block (either polylactide, polyvalerolactone, or polyethylene) and an outer hydrophilic block (polyethylene oxide, PEO). These models exhibit unusual structure very close to the core (clearly an artifact of our model) but which we believe becomes "normal" or bulk-like at relatively short distances from this core. We report on a number of temperature-dependent thermodynamic (structural/energetic) properties as well as kinetic properties. Our observations suggest that under physiological conditions, the hydrophobic regions of these systems may be solid and glassy, with only rare and shallow penetration by water, and that a sharp boundary exists between the hydrophobic cores and either the PEO or water. The PEO in these models is seen to be fully water-solvated at low temperatures but tends to phase separate from water as the temperature is increased, reminiscent of a lower critical solution temperature exhibited by PEO-water mixtures. Water penetration concentration and depth is composition and temperature dependent with greater water penetration for the most ester-rich star polymer.