The integration of dissimilar materials is highly desirable for many different types of device applications but often challenging to achieve in practice. The unrivalled imaging capabilities of the aberration-corrected electron microscope enable enhanced insights to be gained into the atomic arrangements across heterostructured interfaces. This paper provides an overview of our recent observations of oxide-semiconductor heterostructures using aberration-corrected high-angle annular-dark-field and large-angle bright-field imaging modes. The perovskite oxides studied include strontium titanate, barium titanate, and strontium hafnate, which were grown on Si(001) and/or Ge(001) substrates using the techniques of molecular-beam epitaxy or atomic-layer deposition. The oxide layers displayed excellent crystallinity and sharp, abrupt interfaces were observed with no sign of any amorphous interfacial layers. The Ge(001) substrate surfaces invariably showed both 1× and 2× periodicity consistent with preservation of the 2 × 1 surface reconstruction following oxide growth. Overall, the results augur well for the future development of functional oxide-based devices integrated on semiconductor substrates.

The effects of carbon equivalent on thermal and mechanical properties of compacted graphite cast irons were investigated at ambient temperature, 300 and 500 °C, respectively. The group implied the change of carbon content to control the carbon equivalent. The results indicated that with the increasing carbon equivalent from 4.43 to 4.74, the graphite count increase. The thermal conductivity was 48.64, 44.55, 49.04, and 50.36 W/mK for carbon equivalent about 4.43–4.74 of compacted graphite cast irons at ambient temperature, respectively. With an increase in temperature, the thermal conductivity decrease. Moreover, with the increasing carbon equivalent, the tensile strength and yield strength increase initially, and then decrease at ambient temperature, 300 and 500 °C, respectively. With an increase in temperature, the tensile strength and yield strength decrease. Characterization of fracture surface indicated that the mixed ductile-brittle fracture mode prevailed in the compacted graphite cast irons with different carbon equivalents.

Methods used in informatics require input data that are in a machine-readable, structured format. Materials data, in particular, can be exceedingly complex, so defining data formats to store any and all materials-related information is a daunting task. In this article, we discuss a hierarchical data structure used for storing materials data called the physical information file (PIF). The PIF is a flexible schema for storing the structure, processing history, and properties of materials, devices, and physical systems. In addition to a general discussion of the schema, we give examples of its use in representing complex materials systems. We also describe open-source tools that have been developed for building and reading files using the PIF schema.

Spatial hierarchy of microstructure is a defining characteristic of many practical materials systems. Elements of this hierarchy are often realized through nonequilibrium synthesis and process routes, leading to metastable structures that confer specific functionality and enhanced performance. The key to accelerating understanding and developing new and improved materials lies in quantifying microstructure in an unambiguous digital format, employing both physical models and data science methods to explore cause-and-effect relations between structure and properties and relations between composition-dependent process path history and hierarchical microstructure. Given the current state of predictive multiscale modeling, the uncertainties are simply too high to provide necessary decision support in isolation from experiments. Hence, combining experiments and computational modeling with materials data science and informatics provides the only practical path forward in replacing the historical paradigm of empirical materials development. The articles in this issue focus on microstructure informatics, which is relatively less well explored than the use of first-principles combinatorial methods applied to search the space of stable compounds, small molecules, and interface structures.

The goal of the Materials Genome Initiative is to substantially reduce the time and cost of materials design and deployment. Achieving this goal requires taking advantage of the recent advances in data and information sciences. This critical need has impelled the emergence of a new discipline, called materials data science and informatics. This emerging new discipline not only has to address the core scientific/technological challenges related to datafication of materials science and engineering, but also, a number of equally important challenges around data-driven transformation of the current culture, practices, and workflows employed for materials innovation. A comprehensive effort that addresses both of these aspects in a synergistic manner is likely to succeed in realizing the vision of scaled-up materials innovation. Key toolsets needed for the successful adoption of materials data science and informatics in materials innovation are identified and discussed in this article. Prototypical examples of emerging novel toolsets and their functionality are described along with select case studies.

The study of microstructure–property relationships and processing history leading to those relationships is at the core of materials engineering. The historical evolution of the understanding of processing–microstructure–property relationships has largely relied on empirical evidence that, in turn, has helped catalyze theories iteratively linking modeling to experiments, which has then helped the maturation process of materials design. While the power of modeling methods has increased, we have, as of yet, no unified mathematical formalism to seamlessly connect materials chemistry with kinetics and micro- and mesoscale information despite decades of work. In this article, we provide an overview of how “microstructural informatics” permits one to capture the interaction between processing variables and their influence on microstructure–chemistry–property correlations. This includes a particular focus on the use of manifold representations and data compression methods for defining microstructure–chemistry–property relationships that can explain known materials behavior and aid in designing new processing pathways of materials with enhanced properties. The concept of identifying an irreducible representation of microstructure is introduced.

The recent decades have seen significant progress in linking the mechanical performance of materials to their underlying microstructure. This article presents an overview of some of these achievements, trends, and challenges. Attention is given to methods initially developed for micromechanics and their gradual evolution toward powerful multiscale methods. Various methods have been proposed for bridging scales in mechanics of materials, all aiming for efficiency and accuracy. Computational homogenization is one of these powerful approaches, now used systematically for the assessment of structure–property relations. Novel solution methods and model reduction techniques provide tools to speed up the structure–property analysis, whereby large-scale computations have been made possible. Truly fast analyses of microstructures may be expected in the near future.