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.

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.

Rapid increases in global energy use and growing environmental concerns have prompted the development of clean and sustainable alternative energy technologies. Electrical energy storage (EES) is critical for efficiently utilizing electricity produced from intermittent, renewable sources such as solar and wind, as well as for electrifying the transportation sector. Rechargeable batteries are prime candidates for EES, but widespread adoption requires optimization of cost, cycle life, safety, energy density, power density, and environmental impact, all of which are directly linked to severe materials challenges. This article presents a brief overview of the electrode materials currently used in lithium-ion batteries, followed by the challenges and prospects of next-generation insertion-reaction electrodes and conversion-reaction electrodes with a Li+ working ion. Finally, we discuss future directions involving solid electrolytes, multi-electron transfer hosts, and other working ions.