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The economic and social future of nanotechnology depends on our ability and manufacture nanomaterials that avoid potential toxicity, by identifying them before they are made, used and released into the environment. Safety-by-design is a framework for including these issues at an early stage of the development process, but balancing multiple nanoparticle properties and selection criteria remains challenging. Based on a synthetic data set of over 19,000 possible sunscreen product specifications, we have used multi-target machine learning to predict the corresponding size, shape, concentration and polytype of titania nanoparticle additives. The study considers the optical properties responsible for the sun protection factor and product transparency, including the extinction coefficients for ultra violet and visible light, and the potential for toxicity due to the generation of reactive oxygen species from the photocatalytically active facets of both anatase and rutile nanoparticles, as a function of the size and shape. We predict a number of conventional forward structure/property and property/product relationships, but show that a direct structure/product relationship provides superior performance when predicting multiple properties or product specifications simultaneously. These models are then inverted, re-optimized and re-trained to provide focused, high performing inverse design models that do not require additional optimization, and are capable of identifying nanoparticle configurations outside of the training set. The ability to directly predict suitable nanoparticle structures that conform to prerequisite sun protection, transparently and potential toxicity thresholds represents a new approach to safety-by-design that can be applied to other products and materials where multiple design criteria must be met at the same time., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression, and random forest with electronic structure simulations, we find that the degree of twinning (characterized by the fraction of hexagonal closed packed atoms) and the population of the {111} facet (characterized by a surface coordination number of nine) are strongly correlated to the Fermi energy of silver nanoparticles. A concise three layer artificial neural network together with principal component analysis is built to predict this property, with reduced geometrical, structural, and topological features, making the method ideal for efficient and accurate high-throughput screening of large-scale virtual nanoparticle libraries and the creation of single-structure/single-property, multi-structure/single-property, and single-structure/multi-property relationships in the near future.

Although there have been significant advances in the fields of theoretical condensed matter and computational physics, when confronted with the complexity and diversity of nanoparticles available in conventional laboratories a number of modeling challenges remain. These challenges are generally shared among application domains, but the impacts of the limitations and approximations we make to overcome them (or circumvent them) can be more significant one area than another. In the case of nanoparticles for drug delivery applications some immediate challenges include the incompatibility of length-scales, our ability to model weak interactions and solvation, the complexity of the thermochemical environment surrounding the nanoparticles, and the role of polydispersivity in determining properties and performance. Some of these challenges can be met with existing technologies, others with emerging technologies including the data-driven sciences; some others require new methods to be developed. In this article we will briefly review some simple methods and techniques that can be applied to these (and other) challenges, and demonstrate some results using nanodiamond-based drug delivery platforms as an exemplar.

In this work, we have combined constant-pH molecular dynamics simulations and experiments to provide a quantitative analysis of pH dependent interactions between doxorubicin hydrochloride (DOX) cancer therapeutic and faceted nanodiamond (ND) nanoparticle carriers. Our study suggests that when a mixture of faceted ND and DOX is dissolved in a solvent, the pH of this solvent plays a controlling role in the adsorption of DOX molecules on the ND. We find that the binding of DOX molecules on ND occurs only at high pH and requires at least ∼10% of ND surface area to be fully titrated for binding to occur. As such, this study reveals important mechanistic insight underlying an ND-based pH-controlled therapeutic platform.

Fluorescent defects in non-cytotoxic diamond nanoparticles have recently emerged as a preferred candidate for optical labels in biological and medical imaging. The bright fluorescence at 550-800 nm originates from point defects within the particles, some of which appear naturally, while others can be artificially incorporated during synthesis or can be introduced using high-energy ion beam irradiation and subsequent thermal annealing. However, in order for the fluorescent defects to be useful in bio-medical applications there are a number of materials challenges that must be overcome. In this paper, recent studies on nanodiamonds and their use as biolabels are reviewed, while highlighting the links between the physical, chemical and biological issues that arise.

The incorporation of more experimentally relevant parameters into theoretical descriptions of nanomaterials is important for our understanding of the stability of nanostructures in different chemical environments. Using a size-, shape-, and temperature-dependent thermodynamic model we have generated the first phase map for anatase and rutile nanocrystals, that includes both the equilibrium shape and the affects of surface chemistry. The calculated phase map indicates that the equilibrium boundary between anatase and rutile nanocrystals is surface charge chemistry dependent, which relates to both their formation and postsynthesis environments.

The fractal dimension of a surface allows its degree of roughness to be characterized quantitatively. However, limited effort is attempted to calculate the fractal dimension of surfaces computed from precisely known atomic coordinates from computational biomolecular and nanomaterial studies. This work proposes methods to estimate the fractal dimension of the surface of any 3D object composed of spheres, by representing the surface as either a voxelized point cloud or a mathematically exact surface, and computing its box‐counting dimension. Sphractal is published as a Python package that provides these functionalities, and its utility is demonstrated on a set of simulated palladium nanoparticle data. [ABSTRACT FROM AUTHOR]

In this study a new approach to inverse design is presented that draws on the multi‐functionality of nanomaterials and uses sets of properties to predict a unique nanoparticle structure. This approach involves multi‐target regression and uses a precursory forward structure/property prediction to focus the model on the most important characteristics before inverting the problem and simultaneously predicting multiple structural features of a single nanoparticle. The workflow is general, as demonstrated on two nanoparticle data sets, and can rapidly predict property/structure relationships to guide further research and development without the need for additional optimization or high‐throughput sampling. [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]

mime-subtype="jpeg" mimetype="image" xlink:href="S2159685919000363_figAb" xmlns:xlink="http://www.w3.org/1999/xlink" /> Unlike other data intensive domains, understanding distributions, trends, correlations, and relationships in materials data sets typically involves navigating high-dimensional spaces with only a limited number of observations. Under these conditions extracting structure/property relationships is not straightforward and considerable attention must be given to the reduction of feature space before predictions can be made. Here we have used Kohonen networks (self-organizing maps) to identify hidden structure/property relationships in computational sets of twinned and single-crystal diamond nanoparticles based on structural similarity in multiple dimensions, and confirmed the importance of a limited number of surface chemical features using regression. [ABSTRACT FROM AUTHOR]

Abstract: Achieving stability with highly active Ru nanoparticles for electrocatalysis is a major challenge for the oxygen evolution reaction. As improved stability of Ru catalysts has been shown for bulk surfaces with low‐index facets, there is an opportunity to incorporate these stable facets into Ru nanoparticles. Now, a new solution synthesis is presented in which hexagonal close‐packed structured Ru is grown on Au to form nanoparticles with 3D branches. Exposing low‐index facets on these 3D branches creates stable reaction kinetics to achieve high activity and the highest stability observed for Ru nanoparticle oxygen evolution reaction catalysts. These design principles provide a synthetic strategy to achieve stable and active electrocatalysts. [ABSTRACT FROM AUTHOR]

Thefabrication of silicon nanoparticles terminated with halogenspecies provides a convenient route to create readily functionalizablenanostructures; however, the relationship between formation conditionsand the thermodynamic ground-state morphology of the nanoparticlesthus formed remains poorly understood. In this work, we use densityfunctional theory calculations to compute surface energies of siliconsurfaces terminated with fluorine, chlorine, bromine, and iodine asa function of halogen chemical potential and hence we compute, viaa nanomorphology model, the thermodynamically optimal morphology of halogen-terminated silicon nanoparticles. We predict a variety of optimal nanoparticle shapes consisting primarily of Si(100), Si(113),and Si(111) facets with varying terminations, and we demonstrate howcontrol over morphology may be attained by controlling the chemicalenvironment. Implications for the fabrication of nanoparticles withfacet-selective reactivity are also discussed. [ABSTRACT FROM AUTHOR]

While significant progress has been made toward production of monodispersed samples of a variety of nanoparticles, in cases such as diamond nanoparticles (nanodiamonds) a significant degree of polydispersivity persists, so scaling-up of laboratory applications to industrial levels has its challenges. In many cases, however, monodispersivity is not essential for reliable application, provided that the inevitable uncertainties are just as predictable as the functional properties. As computational methods of materials design are becoming more widespread, there is a growing need for robust methods for modeling ensembles of nanoparticles, that capture the structural complexity characteristic of real specimens. In this paper we present a simple statistical approach to modeling of ensembles of nanoparticles, and apply it to nanodiamond, based on sets of individual simulations that have been carefully selected to describe specific structural sources that are responsible for scattering of fundamental properties, and that are typically difficult to eliminate experimentally. For the purposes of demonstration we show how scattering in the Fermi energy and the electronic band gap are related to different structural variations (sources), and how these results can be combined strategically to yield statistically significant predictions of the properties of an entire ensemble of nanodiamonds, rather than merely one individual 'model' particle or a non-representative sub-set. [ABSTRACT FROM AUTHOR]

Abstract: Nanoparticulate hematite is a promising material for catalytic and photoelectrochemical applications, where the surfaces are engineered to improve efficiency in different chemical environments. In the presence of water, the surfaces are typically passivated by hydroxyl groups, which modify the surface stability and reactivity. We use density functional theory and first principles thermodynamics to investigate the low-index surfaces (001), (101), and (104) in hydrous environments. For each of the surfaces, we build various hydroxylation configurations and compare their thermodynamic stability under different environmental conditions (temperature, humidity, and supersaturation of oxygen). The results enable us to construct surface phase diagrams, which provide guidance to the selection of surface structures, and the control of environmental conditions for specific applications. [Copyright &y& Elsevier]

Numerous reports have described the superior properties of nanoparticles and their diverse range of applications. Issues of toxicity, workplace safety and environmental impact have also been a concern. Here we show a theoretical comparison of how the size of titanium dioxide nanoparticles and their concentration in sunscreens can affect efficacy, aesthetics and potential toxicity from free radical production. The simulation results reveal that, unless very small nanoparticles can be shown to be safe, there is no combination of particle size and concentration that will deliver optimal performance in terms of sun protection and aesthetics. Such a theoretical method complements well the experimental approach for identifying these characteristics. [ABSTRACT FROM AUTHOR]

Based on a general definition of a cluster and the quality of a clustering result, a new method for evaluating existing clustering algorithms, or undertaking clustering, capable of predicting the number and type of clusters and outliers present in a data set, regardless of the complexity of the distribution of points, is presented. This algorithm, referred to as iterative label spreading, can recognize the characteristics expected of a successful clustering result before any clustering algorithm is applied, providing a type of hyper‐parameter optimization for clustering. The efficacy of the algorithm, and the assessment of clustering result, are both confirmed using large benchmark two dimensional synthetic data sets, and small multidimensional data describing a set of silver nanoparticles. It is shown that the method is ideal for studying noisy data with high dimensionality and high variance, typical of data captured in materials and nanoscience. [ABSTRACT FROM AUTHOR]