Global markets are pressuring the materials industries to reduce the time span between materials research and materials development. In particular, current approaches to the development and insertion (deployment) of advanced materials in military systems are too time-intensive and expensive. Despite the large investments in defense systems, insertion is not certain, since materials development is often not linked to the system designer needs, with the results frequently being that the materials do not meet their requirements. There is potentially a high payoff for developing methodologies that will accelerate the insertion of materials, thereby saving millions of investment dollars. All industries engaged in developing advanced materials are searching for new methodologies to solve these problems.

Once a new material has been developed, it must be characterized and modeled. Otherwise, components using it cannot be analyzed, and consequently, it cannot be used in engineering products. Therefore, exploiting the full benefits of new (and existing) engineering materials requires specialized informatics tools for data capture, management, analysis, and dissemination. Advances in computing power, coupled with computational modeling and simulation and materials properties databases, will enable materials scientists and engineers to meet these challenges.

The articles in this issue of MRS Bulletin show a variety of applications of the methodologies that are employed in using materials informatics approaches to materials and engineering product development.

The prediction of crystal structure is a key outstanding problem in materials science and one that is fundamental to computational materials design. We argue that by combining the predictive accuracy of quantum mechanics with data mining tools to extract knowledge from a large body of historical experimental or computational results, this problem can be successfully addressed.

We describe some recent advances in the methodology of using electronic structure calculations for materials design. The methods have been developed for the design of ordered metallic alloys and metal alloy catalysts, but the considerations we present are relevant for the atomic-scale computational design of other materials as well. A central problem is how to treat the huge number of compounds that can be envisioned by varying the concentrations and the number of the elements involved. We discuss various strategies for approaching this problem and show how one strategy has led to the computational discovery of a promising catalytic metal alloy surface with high reactivity and low cost.

Data mining of crystal structure databases is performed with methods using deduction, semiempirical heuristic principles, or artificial intelligence to create new information from crystal structure data. In this article, enlightening examples, ranging from a systematic search for new ferroelectric materials to ultrahard, ductile, or high-dielectric-permittivity materials, are detailed, with numerous references providing a handle on the state of the art. This activity is just leaving infancy, especially for its ab initio aspects, with the brightest future ahead. Data mining will clearly guide expensive experiments through inexpensive computations for decades to come. This perspective greatly enhances the value of structure data patiently archived within crystal structure databases for nearly a century now.

To cope with the dynamic social and market demands for advanced materials, new research strategies have to be developed that go beyond the commonly accepted trial-and-error approaches. To this end, a computational materials design platform, digital materials design (DMD), has been created based on well-established fundamental laws, powerful computing, and computational technology. DMD based on computer simulation may produce data that identify overlooked materials behaviors, which then may lead to new theory to explain them, and further to the design of real experiments to fabricate and test the materials. In this review, an illustration of computational methods used in DMD will be given, followed by applications based on two case studies: (1) the design of chemical additives, and (2) the realization of p-type ZnO. Similarly, many effective and efficient materials designs have been performed in the using DMD for various industrial applications, which further demonstrate that DMD, and computational modeling in general, is an invaluable tool for materials discovery.

High-throughput experimentation is effective in systematically producing large and diverse data sets. The marriage of combinatorial materials science and informatics is a natural one, and results are beginning to emerge from the integration of elements of materials informatics with data from combinatorial libraries. We discuss data management issues in high-throughput experimentation and highlight recent examples where data-mining tools are being implemented for extracting knowledge and predicting new compounds, with an emphasis on electronic materials.

This article discusses the need for materials data and materials data management software during product development. Key issues are the changing materials data needs (from general to specific) as the design process proceeds, as well as the requirements for software systems that can automate the key data management tasks: capture, analysis, deployment, and maintenance. Specific issues requiring attention are flexibility of data management, change control, version control, quality control, and traceability of information.

Materials property information such as composition and thermophysical/mechanical properties abound in the literature. Oftentimes, however, the corresponding response curves from which these data are determined are missing or at the very least difficult to retrieve. Further, the paradigm for collecting materials property information has historically centered on (1) properties for materials comparison/selection purposes and (2) input requirements for conventional design/analysis methods. However, just as not all materials are alike or equal, neither are all constitutive models (and thus design/analysis methods) equal; each model typically has its own specific and often unique required materials parameters, some directly measurable and others indirectly measurable. Therefore, the type and extent of materials information routinely collected is not always sufficient to meet the current, much less future, needs of the materials modeling community.

Informatics has been defined as the science concerned with gathering, manipulating, storing, retrieving, and classifying recorded information. A key aspect of informatics is its focus on understanding problems and applying information technology as needed to address those problems. The primary objective of this article is to highlight the need for a paradigm shift in materials data collection, analysis, and dissemination so as to maximize the impact on both practitioners and researchers. Our hope is to identify and articulate what constitutes “sufficient” data content (i.e., quality and quantity) for developing, characterizing, and validating sophisticated nonlinear time- and history-dependent (hereditary) constitutive models. Likewise, the informatics infrastructure required for handling the potentially massive amounts of materials data will be discussed.