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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]

Machine learning can extract complex structure/property relationships but is often insufficient to explain how to control or tune the properties of materials, particularly when they are multi‐functional. This study demonstrates the value of combining multi‐target regression and multi‐target causal graphs to address the need to simultaneously control multiple properties of nanomaterials, and the need to translate these relationships into actionable insights. Using nanodiamonds as an exemplar, recursive feature elimination is first used to identify nine structural features that allow simultaneous prediction of their electron charge transfer properties and thermochemical stability to high accuracy by an interpretable random forest regressor. A multi‐target Bayesian network with domain knowledge incorporated via interactive learning using a hill‐climbing algorithm then determines how these important structural features of nanodiamonds relate to their functional properties, proposing causal paths that can be used to inform experimental design. [ABSTRACT FROM AUTHOR]

Although it has been well established that the stability and properties of graphene oxide nanostructure are strongly influenced by the concentration, type, and distribution of oxygen groups on the surface, there has yet to be a definitive way of predicting the thermochemical stability in advance of detailed and time‐consuming experimentation or simulation. In this study, a data set of over 60 000 unique graphene oxide nanoflakes and supervised machine learning methods are used to predict the probability of observation (stability) with perfect accuracy, based on a limited set of structural features that can be controlled in advance. A decision tree is used to show how the features determine the stability, and a neural network provides an equation to predict the thermodynamic stability of virtually any configuration in minutes. This enables researchers to use machine learning as research planning tool or to assist in analyzing results from microanalysis. [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 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]

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]