The field of materials science and engineering is on the cusp of a digital data revolution. After reviewing the nature of data science and Big Data, we discuss the features of materials data that distinguish them from data in other fields. We introduce the concept of process-structure-property (PSP) linkages and illustrate how the determination of PSPs is one of the main objectives of materials data science. Then we review a selection of materials databases, as well as important aspects of materials data management, such as storage hardware, archiving strategies, and data access strategies. We introduce the emerging field of materials data analytics, which focuses on data-driven approaches to extract and curate materials knowledge from available data sets. The critical need for materials e-collaboration platforms is highlighted, and we conclude the article with a number of suggestions regarding the near-term future of the materials data science field.

A process based on the Kratschmer-Huffman carbon arc method of preparing fullerenes has been used to generate carbon-coated cobalt and cobalt carbide nanocrystallites. Magnetic nanocrystallites are extracted from the soot with a gradient field technique. For Co/C composites, structural characterization by x-ray diffraction and high-resolution transmission electron microscopy reveals the presence of a fcc Co phase, graphite, and a minority ${\mathrm{Co}}_2$C phase. The majority of Co nanocrystals exists as nominally spherical particles, 0.5–5 nm in radius. Hysteretic and temperature-dependent magnetic response, in randomly and magnetically aligned powder samples frozen in epoxy reveals fine-particle magnetism associated with monodomain Co particles. The magnetization exhibits a unique functional dependence on H/T, and hysteresis below a blocking temperature, ${\mathit{T}}_{\mathit{B}}$≃160 K. Below ${\mathit{T}}_{\mathit{B}}$, the temperature dependence of the coercivity is given by ${\mathit{H}}_{\mathit{c}}$=${\mathit{H}}_{\mathit{c}\mathit{i}}$[1-(T/${\mathit{T}}_{\mathit{B}}$ ${)}^{1/2}$], with ${\mathit{H}}_{\mathit{c}\mathit{i}}$≃450 Oe.

Electron Backscatter Diffraction (EBSD) provides a useful means for characterizing microstructure. However, it can be difficult to obtain index-able diffraction patterns from some samples. This can lead to noisy maps reconstructed from the scan data. Various post-processing methodologies have been developed to improve the scan data generally based on correlating non-indexed or mis-indexed points with the orientations obtained at neighboring points in the scan grid. Two new approaches are introduced (1) a re-scanning approach using local pattern averaging and (2) using the multiple solutions obtained by the triplet indexing method. These methodologies are applied to samples with noise introduced into the patterns artificially and by the operational settings of the EBSD camera. They are also applied to a heavily deformed and a fine-grained sample. In all cases, both techniques provide an improvement in the resulting scan data, the local pattern averaging providing the most improvement of the two. However, the local pattern averaging is most helpful when the noise in the patterns is due to the camera operating conditions as opposed to inherent challenges in the sample itself. A byproduct of this study was insight into the validity of various indexing success rate metrics. A metric based given by the fraction of points with CI values greater than some tolerance value (0.1 in this case) was confirmed to provide an accurate assessment of the indexing success rate.

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel’s neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises–Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.