Texture based image classification for nanoparticle surface characterisation and machine learning

Abstract

Restricting materials informatics to the numerical parameters output from conventional materials modelling software restricts us to a subset of machine learning methods capable of uncovering structure/property relationships and driving materials discovery and design. Presented here is a simple way of converting materials structures in to unique image-based fingerprints suitable for image processing methods, that does not require subjective preassessment of the data and selection of descriptors by the user. This combination of methods is shown to classify the morphologies in a set of 425 silver nanoparticles in a meaningful way, and predict the correlation with the energy of the Fermi level in agreement with other machine learning methods that required user intervention. Moving to an image-based, rather than feature list-based, description of nanoparticles and materials brings us one step closer to using experimental micrographs as inputs for machine learning.

Visualising multi-dimensional structure/property relationships with machine learning

“…In the case of the energy of the Fermi level (figure 2(d)) we can see by comparing to figure 2(a) that there is a strong relationship to the external shape label. This confirms that there is a shape-dependent structure/property relationship, even though we have not explicitly included the shapes as a descriptor [6], and is supported by more sophisticated machine learning methods published elsewhere [19]. In general twinned shapes have lower Fermi energies, whereas shapes with high {111} surface area have higher Fermi energies.…”

“…Readers should note that due the the constraints imposed by the crystallographic lattice and the surface orientations characteristic of each zonohedron, it is not possible to generate each shape at all sizes, and the number of atoms in each nanoparticle is different. As described in [6,19,21] this set is characterised using a list of 24 features based on a logical set of descriptors common in computational and experimental materials science, including eight features capturing the local bonding configurations (examples of chemical information typically obtained computationally), six features capturing the bulk structures (examples of materials information that could be obtained from diffraction) and 10 features capturing surface structures (examples of nanoscale information that could be potentially obtained from spectroscopy). Any group of features could be chosen, but the aim here is to test our methods and so a diverse but intuitive list is desirable.…”

“…An alternative dimensional reduction method suitable for materials data set is a self-organisation map (SOM), or Kohonen network [16], which is an unsupervised artificial neural network [17] for nonlinearly mapping high-dimensional spaces into low-dimensional spaces that has the advantage of retaining the intrinsic topological relationship of the input data set. SOMs are also ideal for representing multi-dimensional information as a single two-dimensional plot to form a basis for further analysis [18], and have been recently used to create surface texture maps of nanoparticles for uses as materials fingerprints [19]. The applicability of these methods to multi-dimensional structure/property relationships of complicated sets of materials such as nanoparticles has yet to be established.…”

Adaptive iron-based magnetic nanomaterials of high performance for biomedical applications

“…The trained model can induce a two-fold increase in resolution to retrieve the useful features of the materials. Some novel algorithms were used in the processing of microscopic images to quickly and efficiently characterize nanomaterials in terms of their morphologies [67,68], sizes [69], particle densities [70], and crystallographic defects [71]. Lately, a method applying a genetic algorithm for mass-throughput analysis of the morphologies of nanoparticles is reported [72].…”